Volitional Processes in Changing Physical Activity: A Randomized Controlled Trial with Individuals with Knee Osteoarthritis

Packages

library(car)
library(confintr)
library(dplyr)
library(ggplot2)
library(haven)
library(lavaan)
library(psych)
library(rstatix)
library(semmcci)
library(tableone)

Paper

Sample Descriptives

table <- CreateTableOne(data = data, 
               vars = c("sex_0", # sex
                        "age_0", # age
                        "klgrade_0", # Kellgren-Lawrence grade
                        "gonarthroseyears_0", # disease duration (knee osteoarthritis)
                        "comorbdich_0", # comorbidity
                        "bmi_0", # body mass index
                        "income_0", # income
                        "univdgr_0", # university degree
                        "nation1_0"), # German citizenship
               factorVars = c("sex_0", 
                              "klgrade_0", 
                              "comorbdich_0", 
                              "income_0", 
                              "univdgr_0", 
                              "nation1_0"))

print(table, showAllLevels = TRUE)

##                                 
##                                  level Overall      
##   n                                      241        
##   sex_0 (%)                      0       151 (62.7) 
##                                  1        90 (37.3) 
##   age_0 (mean (SD))                    65.60 (7.61) 
##   klgrade_0 (%)                  2        85 (35.3) 
##                                  3       156 (64.7) 
##   gonarthroseyears_0 (mean (SD))       11.52 (10.21)
##   comorbdich_0 (%)               0        39 (16.4) 
##                                  1       199 (83.6) 
##   bmi_0 (mean (SD))                    28.53 (4.87) 
##   income_0 (%)                   1        17 ( 7.4) 
##                                  2        45 (19.7) 
##                                  3        67 (29.3) 
##                                  4       100 (43.7) 
##   univdgr_0 (%)                  0       132 (55.0) 
##                                  1       108 (45.0) 
##   nation1_0 (%)                  0         4 ( 1.7) 
##                                  1       237 (98.3)

range(data$age_0)

## [1] 44 80

round(range(data$bmi_0),2)

## [1] 19.16 45.79

Attrition Analysis

Continuous Variables

leveneTest(data$acdplan_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0113 0.9156
##       225

summary(aov(data$acdplan_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    0.0  0.0308    0.01  0.921
## Residuals    225  694.6  3.0871               
## 14 observations deleted due to missingness

leveneTest(data$acplan_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  2.2602 0.1341
##       239

summary(aov(data$acplan_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    0.5  0.5454   0.175  0.676
## Residuals    239  746.2  3.1222

leveneTest(data$age_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.3409  0.248
##       239

summary(aov(data$age_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)  
## data$dropout   1    205  205.16   3.577 0.0598 .
## Residuals    239  13709   57.36                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

leveneTest(data$alko1_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.2411 0.6239
##       233

summary(aov(data$alko1_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1     27   26.65   0.817  0.367
## Residuals    233   7600   32.62               
## 6 observations deleted due to missingness

leveneTest(data$alko2_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.4074 0.2367
##       234

summary(aov(data$alko2_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1     84   83.72   1.718  0.191
## Residuals    234  11404   48.73               
## 5 observations deleted due to missingness

leveneTest(data$aufswe_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.9235 0.3375
##       239

summary(aov(data$aufswe_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    0.9  0.9255   0.683  0.409
## Residuals    239  323.8  1.3548

leveneTest(data$bmi_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.2011 0.2742
##       239

summary(aov(data$bmi_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1     30   30.15   1.272  0.261
## Residuals    239   5667   23.71

leveneTest(data$closrelmonths_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1    0.02 0.8877
##       215

summary(aov(data$closrelmonths_0 ~ data$dropout))

##               Df   Sum Sq Mean Sq F value Pr(>F)  
## data$dropout   1   535764  535764   3.707 0.0555 .
## Residuals    215 31069927  144511                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 24 observations deleted due to missingness

leveneTest(data$cesd_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0261 0.8718
##       239

summary(aov(data$cesd_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1      1    1.03    0.02  0.887
## Residuals    239  12221   51.13

leveneTest(data$codplan_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0036 0.9521
##       225

summary(aov(data$codplan_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    0.2  0.1675   0.085  0.771
## Residuals    225  442.4  1.9662               
## 14 observations deleted due to missingness

leveneTest(data$collattitude_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0171 0.8962
##       213

summary(aov(data$collattitude_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    3.6   3.583   1.822  0.179
## Residuals    213  418.9   1.967               
## 26 observations deleted due to missingness

leveneTest(data$collimpint_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0022 0.9629
##       126

summary(aov(data$collimpint_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    0.0   0.007   0.001  0.969
## Residuals    126  558.2   4.430               
## 113 observations deleted due to missingness

leveneTest(data$comorbidities_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.9139 0.3401
##       236

summary(aov(data$comorbidities_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    0.1  0.0605   0.031   0.86
## Residuals    236  458.2  1.9417               
## 3 observations deleted due to missingness

leveneTest(data$coplan_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0477 0.8272
##       238

summary(aov(data$coplan_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    0.4  0.4299   0.172  0.679
## Residuals    238  596.0  2.5042               
## 1 observation deleted due to missingness

leveneTest(data$dplan_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0419  0.838
##       225

summary(aov(data$dplan_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    0.1  0.0841   0.038  0.845
## Residuals    225  495.7  2.2033               
## 14 observations deleted due to missingness

leveneTest(data$erwerbstu_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1   0.023 0.8799
##       78

summary(aov(data$erwerbstu_0 ~ data$dropout))

##              Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout  1    201   200.9   0.768  0.384
## Residuals    78  20414   261.7               
## 161 observations deleted due to missingness

leveneTest(data$gewicht_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0225 0.8808
##       239

summary(aov(data$gewicht_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)  
## data$dropout   1    831   831.4   3.938 0.0483 *
## Residuals    239  50453   211.1                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

leveneTest(data$gonarthroseyears_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value  Pr(>F)  
## group   1  3.0794 0.08061 .
##       232                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(aov(data$gonarthroseyears_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    180   180.1   1.734  0.189
## Residuals    232  24098   103.9               
## 7 observations deleted due to missingness

leveneTest(data$hausstu_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.0528 0.8221
##       12

summary(aov(data$hausstu_0 ~ data$dropout))

##              Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout  1    0.2    0.18   0.002  0.963
## Residuals    12  959.2   79.93               
## 227 observations deleted due to missingness

leveneTest(data$hk_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.3196 0.5724
##       239

summary(aov(data$hk_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    1.0   1.006   0.496  0.482
## Residuals    239  484.6   2.028

leveneTest(data$int_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)  
## group   1  3.0504  0.082 .
##       239                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(aov(data$int_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)  
## data$dropout   1   1.95  1.9543    3.44 0.0649 .
## Residuals    239 135.80  0.5682                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

leveneTest(data$kids2_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value  Pr(>F)  
## group   1  2.8305 0.09418 .
##       184                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(aov(data$kids2_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1   0.09  0.0882   0.108  0.743
## Residuals    184 150.15  0.8160               
## 55 observations deleted due to missingness

leveneTest(data$kidsage1_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.5097 0.4762
##       179

summary(aov(data$kidsage1_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    248   248.4   1.906  0.169
## Residuals    179  23331   130.3               
## 60 observations deleted due to missingness

leveneTest(data$kidsage2_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.8983 0.3455
##       102

summary(aov(data$kidsage2_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1     49   49.28   0.488  0.486
## Residuals    102  10301  100.99               
## 137 observations deleted due to missingness

leveneTest(data$kidsage3_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  1.7653 0.1928
##       34

summary(aov(data$kidsage3_0 ~ data$dropout))

##              Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout  1     26   26.29   0.178  0.675
## Residuals    34   5013  147.44               
## 205 observations deleted due to missingness

leveneTest(data$kidsage4_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.4832  0.513
##        6

summary(aov(data$kidsage4_0 ~ data$dropout))

##              Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout  1    0.1    0.08   0.001   0.98
## Residuals     6  660.8  110.13               
## 233 observations deleted due to missingness

leveneTest(data$lapse3_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  2.3461  0.131
##       58

summary(aov(data$lapse3_0 ~ data$dropout))

##              Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout  1    5.2   5.230   0.959  0.332
## Residuals    58  316.4   5.455               
## 181 observations deleted due to missingness

leveneTest(data$lightagt_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.1279  0.721
##       227

summary(aov(data$lightagt_0 ~ data$dropout))

##               Df  Sum Sq Mean Sq F value Pr(>F)  
## data$dropout   1   34159   34159   5.033 0.0258 *
## Residuals    227 1540624    6787                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 12 observations deleted due to missingness

leveneTest(data$mental_willpower_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0085 0.9264
##       238

summary(aov(data$mental_willpower_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1   0.92  0.9157   0.788  0.376
## Residuals    238 276.49  1.1617               
## 1 observation deleted due to missingness

leveneTest(data$mcs_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.3124 0.5768
##       231

summary(aov(data$mcs_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1     35   34.85   0.327  0.568
## Residuals    231  24657  106.74               
## 8 observations deleted due to missingness

leveneTest(data$clubmembmonths1_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0163 0.8986
##       116

summary(aov(data$clubmembmonths1_0 ~ data$dropout))

##               Df  Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1   75046   75046   0.979  0.325
## Residuals    116 8894355   76675               
## 123 observations deleted due to missingness

leveneTest(data$clubmembmonths2_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.5614 0.4582
##       39

summary(aov(data$clubmembmonths2_0 ~ data$dropout))

##              Df  Sum Sq Mean Sq F value Pr(>F)
## data$dropout  1  110136  110136   0.816  0.372
## Residuals    39 5263326  134957               
## 200 observations deleted due to missingness

leveneTest(data$mobss_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.4287 0.5133
##       225

summary(aov(data$mobss_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1   0.59  0.5938   0.446  0.505
## Residuals    225 299.49  1.3311               
## 14 observations deleted due to missingness

leveneTest(data$modagt_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0015 0.9687
##       227

summary(aov(data$modagt_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1   1668  1668.3   2.273  0.133
## Residuals    227 166577   733.8               
## 12 observations deleted due to missingness

leveneTest(data$motswe_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.1243 0.7247
##       239

summary(aov(data$motswe_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1   0.48  0.4792   0.752  0.387
## Residuals    239 152.37  0.6375

leveneTest(data$muskeltraint_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.5773 0.4481
##       239

summary(aov(data$muskeltraint_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    454   454.1   0.957  0.329
## Residuals    239 113376   474.4

leveneTest(data$mvpaagt_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0587 0.8087
##       227

summary(aov(data$mvpaagt_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)  
## data$dropout   1   2308  2308.4   2.841 0.0933 .
## Residuals    227 184459   812.6                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 12 observations deleted due to missingness

leveneTest(data$neghee_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value  Pr(>F)  
## group   1  4.4894 0.03515 *
##       237                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

oneway.test(data$neghee_0 ~ data$dropout) 

## 
##  One-way analysis of means (not assuming equal variances)
## 
## data:  data$neghee_0 and data$dropout
## F = 1.1495, num df = 1.00, denom df = 104.42, p-value = 0.2861

leveneTest(data$nrecsc_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.4036 0.5259
##       225

summary(aov(data$nrecsc_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    0.6  0.6378   0.404  0.526
## Residuals    225  355.5  1.5801               
## 14 observations deleted due to missingness

leveneTest(data$painvas_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.9473 0.1642
##       234

summary(aov(data$painvas_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    7.6   7.608   1.918  0.167
## Residuals    234  928.2   3.967               
## 5 observations deleted due to missingness

leveneTest(data$pcs_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.4732 0.4922
##       231

summary(aov(data$pcs_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value   Pr(>F)    
## data$dropout   1    599   598.6   11.23 0.000941 ***
## Residuals    231  12316    53.3                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 8 observations deleted due to missingness

leveneTest(data$physical_willpower_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.0241 0.3126
##       238

summary(aov(data$physical_willpower_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)  
## data$dropout   1   5.08   5.078   6.053 0.0146 *
## Residuals    238 199.66   0.839                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness

leveneTest(data$plan_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.6373 0.2019
##       239

summary(aov(data$plan_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    0.5  0.4563   0.217  0.642
## Residuals    239  502.1  2.1008

leveneTest(data$poshee_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value  Pr(>F)  
## group   1  3.7429 0.05422 .
##       238                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(aov(data$poshee_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)  
## data$dropout   1    4.0   3.997   5.352 0.0216 *
## Residuals    238  177.7   0.747                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness

leveneTest(data$precsc_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.6593 0.4177
##       225

summary(aov(data$precsc_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    1.3   1.275   0.659  0.418
## Residuals    225  435.1   1.934               
## 14 observations deleted due to missingness

leveneTest(data$recss_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0489 0.8251
##       225

summary(aov(data$recss_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    0.3  0.3479   0.175  0.676
## Residuals    225  446.6  1.9848               
## 14 observations deleted due to missingness

leveneTest(data$risk_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.9396 0.3334
##       238

summary(aov(data$risk_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    0.3  0.2563   0.143  0.706
## Residuals    238  428.0  1.7981               
## 1 observation deleted due to missingness

leveneTest(data$sedagt_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1   0.942 0.3328
##       227

summary(aov(data$sedagt_0 ~ data$dropout))

##               Df  Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1     240     240    0.02  0.887
## Residuals    227 2692543   11861               
## 12 observations deleted due to missingness

leveneTest(data$smokemonths_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  1.2613 0.2713
##       27

summary(aov(data$smokemonths_0 ~ data$dropout))

##              Df  Sum Sq Mean Sq F value Pr(>F)
## data$dropout  1   41284   41284   0.507  0.482
## Residuals    27 2196622   81356               
## 212 observations deleted due to missingness

leveneTest(data$stepsagt_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.7118 0.3997
##       227

summary(aov(data$stepsagt_0 ~ data$dropout))

##               Df    Sum Sq  Mean Sq F value Pr(>F)  
## data$dropout   1 4.222e+07 42224802     5.9 0.0159 *
## Residuals    227 1.625e+09  7156697                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 12 observations deleted due to missingness

leveneTest(data$swesourceME_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1   2e-04 0.9889
##       238

summary(aov(data$swesourceME_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    0.0  0.0417   0.023  0.878
## Residuals    238  422.2  1.7739               
## 1 observation deleted due to missingness

leveneTest(data$swesourceNA_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value  Pr(>F)  
## group   1  5.0376 0.02576 *
##       228                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

oneway.test(data$swesourceNA_0 ~ data$dropout)

## 
##  One-way analysis of means (not assuming equal variances)
## 
## data:  data$swesourceNA_0 and data$dropout
## F = 5.256, num df = 1.000, denom df = 99.733, p-value = 0.02397

leveneTest(data$swesourcePANA_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.5323  0.217
##       238

summary(aov(data$swesourcePANA_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)  
## data$dropout   1   4.06   4.057   3.751  0.054 .
## Residuals    238 257.44   1.082                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness

leveneTest(data$swesourceVE_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.4987 0.2221
##       238

summary(aov(data$swesourceVE_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    0.2  0.2283   0.093   0.76
## Residuals    238  581.2  2.4420               
## 1 observation deleted due to missingness

leveneTest(data$swesourceVPO_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.9687  0.326
##       238

summary(aov(data$swesourceVPO_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1    6.0   5.965   2.454  0.119
## Residuals    238  578.6   2.431               
## 1 observation deleted due to missingness

leveneTest(data$swesource_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0078 0.9296
##       238

summary(aov(data$swesource_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1   0.02   0.016   0.024  0.877
## Residuals    238 158.99   0.668               
## 1 observation deleted due to missingness

leveneTest(data$tempt_willpower_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.7061 0.4016
##       238

summary(aov(data$tempt_willpower_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1   0.69  0.6877   0.995  0.319
## Residuals    238 164.42  0.6908               
## 1 observation deleted due to missingness

leveneTest(data$validdaysag_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value  Pr(>F)  
## group   1  3.7657 0.05355 .
##       227                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(aov(data$validdaysag_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)  
## data$dropout   1   2.24  2.2392   3.766 0.0536 .
## Residuals    227 134.98  0.5946                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 12 observations deleted due to missingness

leveneTest(data$vigagt_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value  Pr(>F)  
## group   1  6.0032 0.01504 *
##       227                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

oneway.test(data$vigagt_0 ~ data$dropout)

## 
##  One-way analysis of means (not assuming equal variances)
## 
## data:  data$vigagt_0 and data$dropout
## F = 9.6489, num df = 1.00, denom df = 205.82, p-value = 0.002162

leveneTest(data$weartime_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1   3e-04 0.9874
##       227

summary(aov(data$weartime_0 ~ data$dropout))

##               Df  Sum Sq Mean Sq F value Pr(>F)  
## data$dropout   1   55988   55988   5.581  0.019 *
## Residuals    227 2277303   10032                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 12 observations deleted due to missingness

leveneTest(data$wieswe_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.8834 0.1712
##       238

summary(aov(data$wieswe_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1   0.85  0.8478   0.839  0.361
## Residuals    238 240.60  1.0109               
## 1 observation deleted due to missingness

leveneTest(data$willpower_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.1923 0.6614
##       238

summary(aov(data$willpower_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1   0.49  0.4914   0.941  0.333
## Residuals    238 124.22  0.5219               
## 1 observation deleted due to missingness

leveneTest(data$womac_0_i, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value  Pr(>F)  
## group   1   4.384 0.03733 *
##       239                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

oneway.test(data$womac_0_i ~ data$dropout) 

## 
##  One-way analysis of means (not assuming equal variances)
## 
## data:  data$womac_0_i and data$dropout
## F = 4.7878, num df = 1.00, denom df = 107.92, p-value = 0.03082

leveneTest(data$womacfunc_0_i, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)  
## group   1  3.2023 0.0748 .
##       239                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(aov(data$womacfunc_0_i ~ data$dropout)) 

##               Df Sum Sq Mean Sq F value Pr(>F)  
## data$dropout   1   4284    4284   5.882  0.016 *
## Residuals    239 174070     728                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

leveneTest(data$womacpain_0_i, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1   0.885 0.3478
##       239

summary(aov(data$womacpain_0_i ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)  
## data$dropout   1    183  183.00   2.787 0.0963 .
## Residuals    239  15691   65.65                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

leveneTest(data$womacstiff_0_i, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1    0.26 0.6106
##       239

summary(aov(data$womacstiff_0_i ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1     38   37.73   1.859  0.174
## Residuals    239   4850   20.29

leveneTest(data$zig1_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  2.2181  0.148
##       27

summary(aov(data$zig1_0 ~ data$dropout))

##              Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout  1     50   49.99   0.552  0.464
## Residuals    27   2447   90.64               
## 212 observations deleted due to missingness

leveneTest(data$zig1_tag_all_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.8688 0.3523
##       227

summary(aov(data$zig1_tag_all_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1     16   16.22   0.869  0.352
## Residuals    227   4237   18.67               
## 12 observations deleted due to missingness

leveneTest(data$zig2_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.2447 0.6342
##        8

summary(aov(data$zig2_0 ~ data$dropout))

##              Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout  1    469   469.2    0.23  0.644
## Residuals     8  16329  2041.1               
## 231 observations deleted due to missingness

leveneTest(data$zig2_woche_all_0, data$dropout)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.4561 0.5002
##       208

summary(aov(data$zig2_woche_all_0 ~ data$dropout))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$dropout   1     43    43.1   0.456    0.5
## Residuals    208  19655    94.5               
## 31 observations deleted due to missingness

Dichotomous and Categorical Variables

chisq.test(data$aus1_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$aus1_0 and data$dropout
## X-squared = 1.1857, df = 1, p-value = 0.2762

chisq.test(data$aus2_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$aus2_0 and data$dropout
## X-squared = 0.28609, df = 1, p-value = 0.5927

chisq.test(data$aus3_0, data$dropout, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$aus3_0 and data$dropout
## X-squared = 0.00023144, df = NA, p-value = 1

chisq.test(data$aus4_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$aus4_0 and data$dropout
## X-squared = 0.021287, df = 1, p-value = 0.884

chisq.test(data$aus5_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$aus5_0 and data$dropout
## X-squared = 6.2377e-31, df = 1, p-value = 1

chisq.test(data$aus6_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$aus6_0 and data$dropout
## X-squared = 0.62495, df = 1, p-value = 0.4292

chisq.test(data$aus7_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$aus7_0 and data$dropout
## X-squared = 7.3451e-30, df = 1, p-value = 1

chisq.test(data$aus8_0, data$dropout, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$aus8_0 and data$dropout
## X-squared = 0.19086, df = NA, p-value = 1

chisq.test(data$berentet_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$berentet_0 and data$dropout
## X-squared = 0.29229, df = 1, p-value = 0.5888

chisq.test(data$comorbdich_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$comorbdich_0 and data$dropout
## X-squared = 1.9941, df = 1, p-value = 0.1579

chisq.test(data$diet_0, data$dropout, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$diet_0 and data$dropout
## X-squared = 0.61322, df = NA, p-value = 0.5557

chisq.test(data$dietprog1_0, data$dropout, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$dietprog1_0 and data$dropout
## X-squared = 0.82349, df = NA, p-value = 0.5872

chisq.test(data$erwerb_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$erwerb_0 and data$dropout
## X-squared = 0.5911, df = 1, p-value = 0.442

chisq.test(data$fam1_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$fam1_0 and data$dropout
## X-squared = 0.0012802, df = 1, p-value = 0.9715

chisq.test(data$fam2_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$fam2_0 and data$dropout
## X-squared = 7.1429e-31, df = 1, p-value = 1

chisq.test(data$fam3_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$fam3_0 and data$dropout
## X-squared = 0.17896, df = 1, p-value = 0.6723

chisq.test(data$fam4_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$fam4_0 and data$dropout
## X-squared = 0.017692, df = 1, p-value = 0.8942

chisq.test(data$fam5_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$fam5_0 and data$dropout
## X-squared = 0.019958, df = 1, p-value = 0.8877

chisq.test(data$groupfu, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$groupfu and data$dropout
## X-squared = 0.8765, df = 1, p-value = 0.3492

chisq.test(data$groupzmk_1, data$dropout) 

## 
##  Pearson's Chi-squared test
## 
## data:  data$groupzmk_1 and data$dropout
## X-squared = 0.48089, df = 2, p-value = 0.7863

chisq.test(data$haus_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$haus_0 and data$dropout
## X-squared = 1.5648, df = 1, p-value = 0.211

chisq.test(data$income_0, data$dropout, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$income_0 and data$dropout
## X-squared = 3.2206, df = NA, p-value = 0.3613

chisq.test(data$inausb_0, data$dropout, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$inausb_0 and data$dropout
## X-squared = 2.5031, df = NA, p-value = 0.2869

chisq.test(data$klgrade_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$klgrade_0 and data$dropout
## X-squared = 0.062179, df = 1, p-value = 0.8031

chisq.test(data$kids1_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$kids1_0 and data$dropout
## X-squared = 1.9674e-29, df = 1, p-value = 1

chisq.test(data$language1_0, data$dropout, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$language1_0 and data$dropout
## X-squared = 0.31856, df = NA, p-value = 0.6982

chisq.test(data$language2_0, data$dropout, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$language2_0 and data$dropout
## X-squared = 0.13668, df = NA, p-value = 0.7351

chisq.test(data$lapse1_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$lapse1_0 and data$dropout
## X-squared = 0.0077225, df = 1, p-value = 0.93

chisq.test(data$lone_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$lone_0 and data$dropout
## X-squared = 1.1742, df = 1, p-value = 0.2785

chisq.test(data$mitglspo_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$mitglspo_0 and data$dropout
## X-squared = 0.90802, df = 1, p-value = 0.3406

chisq.test(data$nation1_0, data$dropout, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$nation1_0 and data$dropout
## X-squared = 0.02624, df = NA, p-value = 1

chisq.test(data$nation2_0, data$dropout, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$nation2_0 and data$dropout
## X-squared = 0.31856, df = NA, p-value = 0.6797

chisq.test(data$nonerwerb_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$nonerwerb_0 and data$dropout
## X-squared = 0.12549, df = 1, p-value = 0.7232

chisq.test(data$school_0, data$dropout, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$school_0 and data$dropout
## X-squared = 1.7656, df = NA, p-value = 0.937

chisq.test(data$schicht_0, data$dropout, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$schicht_0 and data$dropout
## X-squared = 0.15319, df = NA, p-value = 1

chisq.test(data$sex_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$sex_0 and data$dropout
## X-squared = 0.046549, df = 1, p-value = 0.8292

chisq.test(data$smoke_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$smoke_0 and data$dropout
## X-squared = 0.010267, df = 1, p-value = 0.9193

chisq.test(data$zusels_0, data$dropout, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$zusels_0 and data$dropout
## X-squared = 4.3566, df = NA, p-value = 0.05597

chisq.test(data$zuselt_0, data$dropout, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$zuselt_0 and data$dropout
## X-squared = 0.80904, df = NA, p-value = 0.5767

chisq.test(data$zuspart_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$zuspart_0 and data$dropout
## X-squared = 0.13946, df = 1, p-value = 0.7088

chisq.test(data$zuskids_0, data$dropout) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$zuskids_0 and data$dropout
## X-squared = 1.1314e-30, df = 1, p-value = 1

Logistic Regression

### Logistic regression
summary(glm(dropout ~ gewicht_0 + weartime_0 + lightagt_0 + 
              vigagt_0 + stepsagt_0 + poshee_0 + 
              swesourceNA_0 + womacfunc_0_i + womac_0_i + 
              physical_willpower_0 + pcs_0, 
            data = data, family = binomial))

## 
## Call:
## glm(formula = dropout ~ gewicht_0 + weartime_0 + lightagt_0 + 
##     vigagt_0 + stepsagt_0 + poshee_0 + swesourceNA_0 + womacfunc_0_i + 
##     womac_0_i + physical_willpower_0 + pcs_0, family = binomial, 
##     data = data)
## 
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)  
## (Intercept)           6.148e-01  2.834e+00   0.217   0.8283  
## gewicht_0             8.825e-03  1.369e-02   0.645   0.5191  
## weartime_0           -3.497e-03  2.142e-03  -1.633   0.1025  
## lightagt_0           -2.676e-03  2.478e-03  -1.080   0.2802  
## vigagt_0             -2.509e-01  1.439e-01  -1.743   0.0814 .
## stepsagt_0           -1.043e-04  7.943e-05  -1.313   0.1893  
## poshee_0              5.613e-01  2.436e-01   2.304   0.0212 *
## swesourceNA_0         3.309e-01  1.446e-01   2.288   0.0221 *
## womacfunc_0_i         2.664e-03  3.202e-02   0.083   0.9337  
## womac_0_i            -1.643e-05  2.427e-02  -0.001   0.9995  
## physical_willpower_0  1.312e-01  1.946e-01   0.674   0.5002  
## pcs_0                -3.679e-02  2.713e-02  -1.356   0.1750  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 251.94  on 210  degrees of freedom
## Residual deviance: 214.10  on 199  degrees of freedom
##   (30 observations deleted due to missingness)
## AIC: 238.1
## 
## Number of Fisher Scoring iterations: 5

### Follow-up analysis on positive outcome expectancies at baseline
t.test(data$poshee_0 ~ data$dropout, var.equal = TRUE)

## 
##  Two Sample t-test
## 
## data:  data$poshee_0 by data$dropout
## t = -2.3134, df = 238, p-value = 0.02155
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
##  -0.52793786 -0.04233081
## sample estimates:
## mean in group 0 mean in group 1 
##        4.909552        5.194686

cohen.d(data$poshee_0, data$dropout)

## Call: cohen.d(x = data$poshee_0, group = data$dropout)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,]  0.05   0.33  0.61
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.33
## r equivalent of difference between two means
## data 
## 0.15

### Follow-up analysis on source of self-efficacy: negative affect at baseline
t.test(data$swesourceNA_0 ~ data$dropout, var.equal = FALSE)

## 
##  Welch Two Sample t-test
## 
## data:  data$swesourceNA_0 by data$dropout
## t = -2.2926, df = 99.733, p-value = 0.02397
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
##  -0.79103993 -0.05707536
## sample estimates:
## mean in group 0 mean in group 1 
##        1.780488        2.204545

cohens_d(data, swesourceNA_0 ~ dropout, var.equal = FALSE)

## # A tibble: 1 × 7
##   .y.           group1 group2 effsize    n1    n2 magnitude
## * <chr>         <chr>  <chr>    <dbl> <int> <int> <ord>    
## 1 swesourceNA_0 0      1       -0.350   164    66 small

Randomization Check

Continuous Variables

leveneTest(data$acdplan_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.3241 0.5697
##       225

summary(aov(data$acdplan_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    0.8  0.7888   0.256  0.614
## Residuals    225  693.8  3.0837               
## 14 observations deleted due to missingness

leveneTest(data$acplan_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0179 0.8936
##       239

summary(aov(data$acplan_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    0.1  0.0541   0.017  0.895
## Residuals    239  746.7  3.1242

leveneTest(data$age_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.3451 0.5575
##       239

summary(aov(data$age_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1      5     4.8   0.082  0.774
## Residuals    239  13909    58.2

leveneTest(data$alko1_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0846 0.7715
##       233

summary(aov(data$alko1_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1      1    1.34   0.041   0.84
## Residuals    233   7625   32.73               
## 6 observations deleted due to missingness

leveneTest(data$alko2_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.4381 0.5087
##       234

summary(aov(data$alko2_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1      4    4.17   0.085  0.771
## Residuals    234  11483   49.07               
## 5 observations deleted due to missingness

leveneTest(data$aufswe_0, data$groupfu) 

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)  
## group   1  4.0919 0.0442 *
##       239                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

oneway.test(data$aufswe_0 ~ data$groupfu)

## 
##  One-way analysis of means (not assuming equal variances)
## 
## data:  data$aufswe_0 and data$groupfu
## F = 3.206, num df = 1.00, denom df = 233.23, p-value = 0.07466

leveneTest(data$bmi_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.9053 0.1688
##       239

summary(aov(data$bmi_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1      2   1.528   0.064    0.8
## Residuals    239   5695  23.829

leveneTest(data$closrelmonths_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.1893 0.2767
##       215

summary(aov(data$closrelmonths_0 ~ data$groupfu))

##               Df   Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    11836   11836   0.081  0.777
## Residuals    215 31593854  146948               
## 24 observations deleted due to missingness

leveneTest(data$cesd_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  2.2609  0.134
##       239

summary(aov(data$cesd_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1      1    0.76   0.015  0.903
## Residuals    239  12221   51.13

leveneTest(data$codplan_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0049 0.9441
##       225

summary(aov(data$codplan_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    0.0  0.0201    0.01   0.92
## Residuals    225  442.5  1.9669               
## 14 observations deleted due to missingness

leveneTest(data$collattitude_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1   0.421 0.5171
##       213

summary(aov(data$collattitude_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    0.0   0.018   0.009  0.924
## Residuals    213  422.5   1.983               
## 26 observations deleted due to missingness

leveneTest(data$collimpint_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0315 0.8595
##       126

summary(aov(data$collimpint_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    0.4   0.356    0.08  0.777
## Residuals    126  557.8   4.427               
## 113 observations deleted due to missingness

leveneTest(data$comorbidities_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.1296 0.7192
##       236

summary(aov(data$comorbidities_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    0.6  0.6129   0.316  0.575
## Residuals    236  457.7  1.9393               
## 3 observations deleted due to missingness

leveneTest(data$coplan_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.2727  0.602
##       238

summary(aov(data$coplan_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    0.3  0.3403   0.136  0.713
## Residuals    238  596.1  2.5046               
## 1 observation deleted due to missingness

leveneTest(data$dplan_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0077 0.9301
##       225

summary(aov(data$dplan_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    0.2  0.2319   0.105  0.746
## Residuals    225  495.6  2.2026               
## 14 observations deleted due to missingness

leveneTest(data$erwerbstu_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.2268 0.6352
##       78

summary(aov(data$erwerbstu_0 ~ data$groupfu))

##              Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu  1    188   187.6   0.716    0.4
## Residuals    78  20427   261.9               
## 161 observations deleted due to missingness

leveneTest(data$gewicht_0, data$groupfu) 

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value  Pr(>F)  
## group   1  4.6513 0.03203 *
##       239                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

oneway.test(data$gewicht_0 ~ data$groupfu)

## 
##  One-way analysis of means (not assuming equal variances)
## 
## data:  data$gewicht_0 and data$groupfu
## F = 0.017165, num df = 1.0, denom df = 226.4, p-value = 0.8959

leveneTest(data$gonarthroseyears_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1   2e-04 0.9884
##       232

summary(aov(data$gonarthroseyears_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1      3    3.11    0.03  0.863
## Residuals    232  24275  104.64               
## 7 observations deleted due to missingness

leveneTest(data$hausstu_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.5611 0.4682
##       12

summary(aov(data$hausstu_0 ~ data$groupfu))

##              Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu  1   47.4   47.44   0.624  0.445
## Residuals    12  911.9   75.99               
## 227 observations deleted due to missingness

leveneTest(data$hk_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.0723 0.3015
##       239

summary(aov(data$hk_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    1.5   1.543   0.762  0.384
## Residuals    239  484.1   2.025

leveneTest(data$int_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.2201 0.6394
##       239

summary(aov(data$int_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1   0.29  0.2925   0.509  0.476
## Residuals    239 137.46  0.5751

leveneTest(data$kids2_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.2716 0.6029
##       184

summary(aov(data$kids2_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1   0.25  0.2545   0.312  0.577
## Residuals    184 149.98  0.8151               
## 55 observations deleted due to missingness

leveneTest(data$kidsage1_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0665 0.7968
##       179

summary(aov(data$kidsage1_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    121   121.0   0.924  0.338
## Residuals    179  23459   131.1               
## 60 observations deleted due to missingness

leveneTest(data$kidsage2_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1   0.569 0.4524
##       102

summary(aov(data$kidsage2_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1     11   11.08   0.109  0.742
## Residuals    102  10339  101.37               
## 137 observations deleted due to missingness

leveneTest(data$kidsage3_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  1.8022 0.1883
##       34

summary(aov(data$kidsage3_0 ~ data$groupfu))

##              Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu  1     38   38.27    0.26  0.613
## Residuals    34   5001  147.09               
## 205 observations deleted due to missingness

leveneTest(data$kidsage4_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  1.7479 0.2343
##        6

summary(aov(data$kidsage4_0 ~ data$groupfu))

##              Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu  1    1.0    1.01   0.009  0.927
## Residuals     6  659.9  109.98               
## 233 observations deleted due to missingness

leveneTest(data$lapse3_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.3884 0.5356
##       58

summary(aov(data$lapse3_0 ~ data$groupfu))

##              Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu  1    2.3   2.279   0.414  0.523
## Residuals    58  319.4   5.506               
## 181 observations deleted due to missingness

leveneTest(data$lightagt_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.0768 0.3005
##       227

summary(aov(data$lightagt_0 ~ data$groupfu))

##               Df  Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    1083    1083   0.156  0.693
## Residuals    227 1573700    6933               
## 12 observations deleted due to missingness

leveneTest(data$mental_willpower_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0419 0.8379
##       238

summary(aov(data$mental_willpower_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1   0.03  0.0331   0.028  0.866
## Residuals    238 277.37  1.1654               
## 1 observation deleted due to missingness

leveneTest(data$mcs_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.9082 0.3416
##       231

summary(aov(data$mcs_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    114   113.9   1.071  0.302
## Residuals    231  24578   106.4               
## 8 observations deleted due to missingness

leveneTest(data$clubmembmonths1_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.2374  0.627
##       116

summary(aov(data$clubmembmonths1_0 ~ data$groupfu))

##               Df  Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1   61260   61260   0.798  0.374
## Residuals    116 8908140   76794               
## 123 observations deleted due to missingness

leveneTest(data$clubmembmonths2_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.0244 0.8767
##       39

summary(aov(data$clubmembmonths2_0 ~ data$groupfu))

##              Df  Sum Sq Mean Sq F value Pr(>F)
## data$groupfu  1   21550   21550   0.157  0.694
## Residuals    39 5351912  137229               
## 200 observations deleted due to missingness

leveneTest(data$mobss_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.5202 0.4715
##       225

summary(aov(data$mobss_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    0.9  0.9032   0.679  0.411
## Residuals    225  299.2  1.3297               
## 14 observations deleted due to missingness

leveneTest(data$modagt_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0016 0.9682
##       227

summary(aov(data$modagt_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    192   191.7   0.259  0.611
## Residuals    227 168054   740.3               
## 12 observations deleted due to missingness

leveneTest(data$motswe_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0526 0.8188
##       239

summary(aov(data$motswe_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1   0.03  0.0308   0.048  0.826
## Residuals    239 152.82  0.6394

leveneTest(data$muskeltraint_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.7248 0.3954
##       239

summary(aov(data$muskeltraint_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    346   346.0   0.729  0.394
## Residuals    239 113484   474.8

leveneTest(data$mvpaagt_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0078 0.9297
##       227

summary(aov(data$mvpaagt_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    224   224.1   0.273  0.602
## Residuals    227 186544   821.8               
## 12 observations deleted due to missingness

leveneTest(data$neghee_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.4397 0.5079
##       237

summary(aov(data$neghee_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1   0.01  0.0068   0.009  0.924
## Residuals    237 175.96  0.7424               
## 2 observations deleted due to missingness

leveneTest(data$nrecsc_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.5692 0.4514
##       225

summary(aov(data$nrecsc_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    0.9  0.8987   0.569  0.451
## Residuals    225  355.3  1.5789               
## 14 observations deleted due to missingness

leveneTest(data$painvas_0, data$groupfu) 

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.4638 0.4965
##       234

summary(aov(data$painvas_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)  
## data$groupfu   1   20.7  20.696   5.292 0.0223 *
## Residuals    234  915.1   3.911                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 5 observations deleted due to missingness

leveneTest(data$pcs_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.7512  0.387
##       231

summary(aov(data$pcs_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    116   116.0   2.093  0.149
## Residuals    231  12798    55.4               
## 8 observations deleted due to missingness

leveneTest(data$physical_willpower_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.6959 0.1941
##       238

summary(aov(data$physical_willpower_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1   0.05  0.0454   0.053  0.818
## Residuals    238 204.69  0.8600               
## 1 observation deleted due to missingness

leveneTest(data$plan_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.7808 0.3778
##       239

summary(aov(data$plan_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    0.2  0.2058   0.098  0.755
## Residuals    239  502.3  2.1019

leveneTest(data$poshee_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0236 0.8779
##       238

summary(aov(data$poshee_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1   0.01  0.0082   0.011  0.918
## Residuals    238 181.73  0.7636               
## 1 observation deleted due to missingness

leveneTest(data$precsc_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1   0.077 0.7817
##       225

summary(aov(data$precsc_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    0.1  0.1493   0.077  0.782
## Residuals    225  436.3  1.9389               
## 14 observations deleted due to missingness

leveneTest(data$recss_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1   0.039 0.8437
##       225

summary(aov(data$recss_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    1.1   1.076   0.543  0.462
## Residuals    225  445.9   1.982               
## 14 observations deleted due to missingness

leveneTest(data$risk_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1   1.635 0.2023
##       238

summary(aov(data$risk_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    0.9  0.8938   0.498  0.481
## Residuals    238  427.3  1.7955               
## 1 observation deleted due to missingness

leveneTest(data$sedagt_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.2323 0.2681
##       227

summary(aov(data$sedagt_0 ~ data$groupfu))

##               Df  Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    3251    3251   0.274  0.601
## Residuals    227 2689533   11848               
## 12 observations deleted due to missingness

leveneTest(data$smokemonths_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.6533  0.426
##       27

summary(aov(data$smokemonths_0 ~ data$groupfu))

##              Df  Sum Sq Mean Sq F value Pr(>F)
## data$groupfu  1   49774   49774   0.614   0.44
## Residuals    27 2188132   81042               
## 212 observations deleted due to missingness

leveneTest(data$stepsagt_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.1089 0.7417
##       227

summary(aov(data$stepsagt_0 ~ data$groupfu))

##               Df    Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1 4.619e+05  461934   0.063  0.802
## Residuals    227 1.666e+09 7340674               
## 12 observations deleted due to missingness

leveneTest(data$swesourceME_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.6365 0.4258
##       238

summary(aov(data$swesourceME_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    0.1  0.1278   0.072  0.789
## Residuals    238  422.1  1.7736               
## 1 observation deleted due to missingness

leveneTest(data$swesourceNA_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  2.0097 0.1577
##       228

summary(aov(data$swesourceNA_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)  
## data$groupfu   1    4.1   4.097   3.043 0.0824 .
## Residuals    228  306.9   1.346                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 11 observations deleted due to missingness

leveneTest(data$swesourcePANA_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.8216 0.1784
##       238

summary(aov(data$swesourcePANA_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1   0.48  0.4792   0.437  0.509
## Residuals    238 261.02  1.0967               
## 1 observation deleted due to missingness

leveneTest(data$swesourceVE_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.2654 0.2618
##       238

summary(aov(data$swesourceVE_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    3.2   3.245   1.336  0.249
## Residuals    238  578.2   2.429               
## 1 observation deleted due to missingness

leveneTest(data$swesourceVPO_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1   0.234  0.629
##       238

summary(aov(data$swesourceVPO_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    1.7   1.710   0.698  0.404
## Residuals    238  582.9   2.449               
## 1 observation deleted due to missingness

leveneTest(data$swesource_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  2.5649 0.1106
##       238

summary(aov(data$swesource_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1   0.06  0.0595   0.089  0.766
## Residuals    238 158.94  0.6678               
## 1 observation deleted due to missingness

leveneTest(data$tempt_willpower_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0483 0.8262
##       238

summary(aov(data$tempt_willpower_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1   0.07  0.0699   0.101  0.751
## Residuals    238 165.04  0.6934               
## 1 observation deleted due to missingness

leveneTest(data$validdaysag_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.6494 0.4212
##       227

summary(aov(data$validdaysag_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1   0.39  0.3915   0.649  0.421
## Residuals    227 136.83  0.6028               
## 12 observations deleted due to missingness

leveneTest(data$vigagt_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0833 0.7731
##       227

summary(aov(data$vigagt_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    0.3  0.3246   0.123  0.726
## Residuals    227  597.0  2.6301               
## 12 observations deleted due to missingness

leveneTest(data$weartime_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.1833 0.2778
##       227

summary(aov(data$weartime_0 ~ data$groupfu))

##               Df  Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1    1333    1333    0.13  0.719
## Residuals    227 2331957   10273               
## 12 observations deleted due to missingness

leveneTest(data$wieswe_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.6414  0.424
##       238

summary(aov(data$wieswe_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)  
## data$groupfu   1   3.67   3.667   3.671 0.0566 .
## Residuals    238 237.78   0.999                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness

leveneTest(data$willpower_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.0669 0.7962
##       238

summary(aov(data$willpower_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1   0.01  0.0103    0.02  0.888
## Residuals    238 124.70  0.5240               
## 1 observation deleted due to missingness

leveneTest(data$womac_0_i, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value  Pr(>F)  
## group   1   3.511 0.06218 .
##       239                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(aov(data$womac_0_i ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1   1018    1018    0.77  0.381
## Residuals    239 316203    1323

leveneTest(data$womacfunc_0_i, data$groupfu) 

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value  Pr(>F)  
## group   1  4.3938 0.03712 *
##       239                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

oneway.test(data$womacfunc_0_i ~ data$groupfu) 

## 
##  One-way analysis of means (not assuming equal variances)
## 
## data:  data$womacfunc_0_i and data$groupfu
## F = 0.62436, num df = 1.00, denom df = 228.11, p-value = 0.4303

leveneTest(data$womacpain_0_i, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.3473 0.5562
##       239

summary(aov(data$womacpain_0_i ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1     32   32.18   0.486  0.487
## Residuals    239  15842   66.28

leveneTest(data$womacstiff_0_i, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.5956  0.441
##       239

summary(aov(data$womacstiff_0_i ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1     21   21.14   1.038  0.309
## Residuals    239   4867   20.36

leveneTest(data$zig1_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.0647 0.8012
##       27

summary(aov(data$zig1_0 ~ data$groupfu))

##              Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu  1    0.1    0.08   0.001  0.977
## Residuals    27 2497.1   92.48               
## 212 observations deleted due to missingness

leveneTest(data$zig1_tag_all_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  1.3028 0.2549
##       227

summary(aov(data$zig1_tag_all_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1     24   24.27   1.303  0.255
## Residuals    227   4229   18.63               
## 12 observations deleted due to missingness

leveneTest(data$zig2_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  1.3849 0.2731
##        8

summary(aov(data$zig2_0 ~ data$groupfu))

##              Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu  1   2516    2516   1.409  0.269
## Residuals     8  14283    1785               
## 231 observations deleted due to missingness

leveneTest(data$zig2_woche_all_0, data$groupfu)

## Levene's Test for Homogeneity of Variance (center = median)
##        Df F value Pr(>F)
## group   1  0.6548 0.4193
##       208

summary(aov(data$zig2_woche_all_0 ~ data$groupfu))

##               Df Sum Sq Mean Sq F value Pr(>F)
## data$groupfu   1     62   61.82   0.655  0.419
## Residuals    208  19636   94.41               
## 31 observations deleted due to missingness

Dichotomous and Categorical Variables

chisq.test(data$aus1_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$aus1_0 and data$groupfu
## X-squared = 0.90611, df = 1, p-value = 0.3411

chisq.test(data$aus2_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$aus2_0 and data$groupfu
## X-squared = 4.8096e-30, df = 1, p-value = 1

chisq.test(data$aus3_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$aus3_0 and data$groupfu
## X-squared = 0.11541, df = 1, p-value = 0.7341

chisq.test(data$aus4_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$aus4_0 and data$groupfu
## X-squared = 0.0077541, df = 1, p-value = 0.9298

chisq.test(data$aus5_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$aus5_0 and data$groupfu
## X-squared = 1.5751, df = 1, p-value = 0.2095

chisq.test(data$aus6_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$aus6_0 and data$groupfu
## X-squared = 0.075465, df = 1, p-value = 0.7835

chisq.test(data$aus7_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$aus7_0 and data$groupfu
## X-squared = 5.8997e-32, df = 1, p-value = 1

chisq.test(data$aus8_0, data$groupfu, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$aus8_0 and data$groupfu
## X-squared = 1.9424, df = NA, p-value = 0.2009

chisq.test(data$berentet_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$berentet_0 and data$groupfu
## X-squared = 0.014641, df = 1, p-value = 0.9037

chisq.test(data$comorbdich_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$comorbdich_0 and data$groupfu
## X-squared = 0.8762, df = 1, p-value = 0.3492

chisq.test(data$diet_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$diet_0 and data$groupfu
## X-squared = 0.0070077, df = 1, p-value = 0.9333

chisq.test(data$dietprog1_0, data$groupfu, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$dietprog1_0 and data$groupfu
## X-squared = 2.1213, df = NA, p-value = 0.2534

chisq.test(data$erwerb_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$erwerb_0 and data$groupfu
## X-squared = 0.31669, df = 1, p-value = 0.5736

chisq.test(data$fam1_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$fam1_0 and data$groupfu
## X-squared = 2.7649, df = 1, p-value = 0.09635

chisq.test(data$fam2_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$fam2_0 and data$groupfu
## X-squared = 9.5737e-31, df = 1, p-value = 1

chisq.test(data$fam3_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$fam3_0 and data$groupfu
## X-squared = 6.1378, df = 1, p-value = 0.01323

chisq.test(data$fam4_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$fam4_0 and data$groupfu
## X-squared = 0.87045, df = 1, p-value = 0.3508

chisq.test(data$fam5_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$fam5_0 and data$groupfu
## X-squared = 2.3647, df = 1, p-value = 0.1241

chisq.test(data$haus_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$haus_0 and data$groupfu
## X-squared = 0.10388, df = 1, p-value = 0.7472

chisq.test(data$income_0, data$groupfu) 

## 
##  Pearson's Chi-squared test
## 
## data:  data$income_0 and data$groupfu
## X-squared = 1.4208, df = 3, p-value = 0.7007

chisq.test(data$inausb_0, data$groupfu, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$inausb_0 and data$groupfu
## X-squared = 1.0467, df = NA, p-value = 0.4713

chisq.test(data$klgrade_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$klgrade_0 and data$groupfu
## X-squared = 0.0010171, df = 1, p-value = 0.9746

chisq.test(data$kids1_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$kids1_0 and data$groupfu
## X-squared = 2.5023, df = 1, p-value = 0.1137

chisq.test(data$language1_0, data$groupfu, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$language1_0 and data$groupfu
## X-squared = 0.0035632, df = NA, p-value = 1

chisq.test(data$language2_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$language2_0 and data$groupfu
## X-squared = 8.8225e-31, df = 1, p-value = 1

chisq.test(data$lapse1_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$lapse1_0 and data$groupfu
## X-squared = 0.4207, df = 1, p-value = 0.5166

chisq.test(data$lone_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$lone_0 and data$groupfu
## X-squared = 1.1021, df = 1, p-value = 0.2938

chisq.test(data$mitglspo_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$mitglspo_0 and data$groupfu
## X-squared = 0.1543, df = 1, p-value = 0.6945

chisq.test(data$nation1_0, data$groupfu, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$nation1_0 and data$groupfu
## X-squared = 1.1035, df = NA, p-value = 0.3888

chisq.test(data$nation2_0, data$groupfu, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$nation2_0 and data$groupfu
## X-squared = 0.60683, df = NA, p-value = 0.4818

chisq.test(data$nonerwerb_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$nonerwerb_0 and data$groupfu
## X-squared = 0.1133, df = 1, p-value = 0.7364

chisq.test(data$school_0, data$groupfu, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$school_0 and data$groupfu
## X-squared = 3.1642, df = NA, p-value = 0.7361

chisq.test(data$schicht_0, data$groupfu, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$schicht_0 and data$groupfu
## X-squared = 2.4846, df = NA, p-value = 0.1679

chisq.test(data$sex_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$sex_0 and data$groupfu
## X-squared = 0, df = 1, p-value = 1

chisq.test(data$smoke_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$smoke_0 and data$groupfu
## X-squared = 0.81328, df = 1, p-value = 0.3672

chisq.test(data$zusels_0, data$groupfu, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$zusels_0 and data$groupfu
## X-squared = 0.0026497, df = NA, p-value = 1

chisq.test(data$zuselt_0, data$groupfu, simulate.p.value = T) 

## 
##  Pearson's Chi-squared test with simulated p-value (based on 2000 replicates)
## 
## data:  data$zuselt_0 and data$groupfu
## X-squared = 0.00086844, df = NA, p-value = 1

chisq.test(data$zuspart_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$zuspart_0 and data$groupfu
## X-squared = 1.395, df = 1, p-value = 0.2376

chisq.test(data$zuskids_0, data$groupfu) 

## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  data$zuskids_0 and data$groupfu
## X-squared = 1.7963, df = 1, p-value = 0.1802

Logistic Regression

### Logistic regression
summary(glm(groupfu ~ painvas_0 + fam3_0, data = data, family = binomial))

## 
## Call:
## glm(formula = groupfu ~ painvas_0 + fam3_0, family = binomial, 
##     data = data)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.64641    0.27477   2.352  0.01865 * 
## painvas_0   -0.13848    0.06824  -2.029  0.04244 * 
## fam3_0      -0.97790    0.37694  -2.594  0.00948 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 325.77  on 234  degrees of freedom
## Residual deviance: 313.51  on 232  degrees of freedom
##   (6 observations deleted due to missingness)
## AIC: 319.51
## 
## Number of Fisher Scoring iterations: 4

### Follow-up analysis on pain in the affected knee at baseline
t.test(data$painvas_0 ~ data$groupfu, var.equal = TRUE)

## 
##  Two Sample t-test
## 
## data:  data$painvas_0 by data$groupfu
## t = 2.3004, df = 234, p-value = 0.0223
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
##  0.08503963 1.09953697
## sample estimates:
## mean in group 0 mean in group 1 
##        3.756154        3.163866

cohen.d(data$painvas_0, data$groupfu)

## Call: cohen.d(x = data$painvas_0, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.56   -0.3 -0.04
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.3
## r equivalent of difference between two means
##  data 
## -0.15

### Follow-up analysis on being divorced
chisq.test(data$fam3_0, data$groupfu, correct = FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  data$fam3_0 and data$groupfu
## X-squared = 7.0003, df = 1, p-value = 0.008149

cramersv(data[c("fam3_0", "groupfu")])

## [1] 0.1707865

Table 1

Means and Standard Deviations

### MVPA at baseline
describeBy(mvpaagt_0 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##           vars   n  mean    sd median trimmed   mad  min    max  range skew kurtosis   se
## mvpaagt_0    1 113 48.42 28.21   45.2   45.15 27.28 8.29 141.15 132.87 1.14     1.41 2.65
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##           vars   n  mean    sd median trimmed   mad  min    max  range skew kurtosis  se
## mvpaagt_0    1 116 46.44 29.11  43.29   43.41 23.09 0.83 141.15 140.32 1.07     1.48 2.7

### MVPA at 12 months
describeBy(mvpaagt_12 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##            vars  n  mean    sd median trimmed   mad  min    max  range skew kurtosis   se
## mvpaagt_12    1 76 49.64 29.25   43.3   45.98 29.76 1.86 131.14 129.29 0.96     0.38 3.36
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##            vars  n  mean    sd median trimmed   mad min    max  range skew kurtosis   se
## mvpaagt_12    1 71 40.69 25.83  37.71   37.14 19.44   6 131.15 125.15 1.27     1.66 3.07

### MVPA at 24 months
describeBy(mvpaagt_24 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##            vars  n  mean    sd median trimmed   mad min max range skew kurtosis   se
## mvpaagt_24    1 66 41.01 27.03  34.09   38.77 22.58 2.8 110 107.2 0.71     -0.4 3.33
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##            vars  n mean    sd median trimmed   mad  min    max range skew kurtosis   se
## mvpaagt_24    1 68 37.6 26.33  33.08    34.1 20.25 3.71 125.51 121.8 1.45     2.27 3.19

### Muscle strength training at baseline
describeBy(muskeltraint_0 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##                vars   n  mean    sd median trimmed   mad min   max range skew kurtosis   se
## muskeltraint_0    1 118 17.38 23.23   8.57   12.79 12.71   0 77.15 77.15 1.47      1.1 2.14
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##                vars   n  mean    sd median trimmed   mad min   max range skew kurtosis   se
## muskeltraint_0    1 123 14.98 20.32   8.57   10.91 12.71   0 77.15 77.15 1.47      1.2 1.83

### Muscle strength training at 6 months
describeBy(muskeltraint_6 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##                vars  n  mean    sd median trimmed  mad min   max range skew kurtosis   se
## muskeltraint_6    1 88 13.27 18.58   4.29    9.61 6.35   0 68.57 68.57  1.5     1.32 1.98
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##                vars  n  mean    sd median trimmed   mad min max range skew kurtosis   se
## muskeltraint_6    1 85 16.99 16.74     15   14.97 22.24   0  60    60 0.77    -0.29 1.82

### Muscle strength training at 12 months
describeBy(muskeltraint_12 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##                 vars  n  mean    sd median trimmed   mad min   max range skew kurtosis  se
## muskeltraint_12    1 86 15.85 21.29   8.57   11.72 12.71   0 85.71 85.71 1.53     1.45 2.3
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##                 vars  n  mean    sd median trimmed   mad min   max range skew kurtosis   se
## muskeltraint_12    1 77 21.96 25.13  12.86   17.86 19.06   0 90.01 90.01 1.25     0.74 2.86

### Muscle strength training at 18 months
describeBy(muskeltraint_18 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##                 vars  n  mean    sd median trimmed   mad min   max range skew kurtosis   se
## muskeltraint_18    1 80 17.95 20.36  12.86   14.87 19.06   0 77.15 77.15 0.97    -0.08 2.28
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##                 vars  n  mean    sd median trimmed   mad min   max range skew kurtosis   se
## muskeltraint_18    1 72 18.12 18.84  13.93   15.55 20.65   0 77.14 77.14 1.02     0.44 2.22

### Muscle strength training at 24 months
describeBy(muskeltraint_24 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##                 vars  n  mean    sd median trimmed   mad min    max  range skew kurtosis   se
## muskeltraint_24    1 87 21.97 27.31   8.57   17.23 12.71   0 107.15 107.15 1.36     1.17 2.93
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##                 vars  n  mean    sd median trimmed   mad min    max  range skew kurtosis   se
## muskeltraint_24    1 84 22.42 28.03  13.93   16.86 20.65   0 107.15 107.15 1.58     1.89 3.06

### Action planning at baseline
describeBy(acplan_0 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##          vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
## acplan_0    1 118 3.99 1.76   4.25     4.1 2.22   1   6     5 -0.46    -1.15 0.16
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##          vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
## acplan_0    1 123 3.96 1.77   4.25    4.07 2.22   1   6     5 -0.44    -1.16 0.16

### Action planning at 6 months
describeBy(acplan_6 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##          vars  n mean   sd median trimmed  mad min max range  skew kurtosis  se
## acplan_6    1 85 4.14 1.81      5    4.29 1.48   1   6     5 -0.63    -0.99 0.2
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##          vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## acplan_6    1 84 4.64 1.66      5    4.91 1.48   1   6     5 -1.07    -0.13 0.18

### Action planning at 12 months
describeBy(acplan_12 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##           vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## acplan_12    1 84 4.09 1.79   4.75    4.22 1.85   1   6     5 -0.59    -1.06 0.19
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##           vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## acplan_12    1 75 4.54 1.58      5    4.76 1.48   1   6     5 -0.93    -0.31 0.18

### Action planning at 18 months
describeBy(acplan_18 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##           vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## acplan_18    1 80 4.23 1.71   4.75    4.41 1.85   1   6     5 -0.63    -0.95 0.19
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##           vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## acplan_18    1 71 4.59 1.55      5    4.85 1.48   1   6     5 -1.1     0.14 0.18

### Action planning at 24 months
describeBy(acplan_24 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##           vars  n mean   sd median trimmed  mad min max range skew kurtosis  se
## acplan_24    1 87 3.65 1.87      4    3.68 2.97   1   6     5 -0.2    -1.44 0.2
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##           vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## acplan_24    1 83 4.27 1.73      5    4.46 1.48   1   6     5 -0.63    -0.89 0.19

### Coping planning at baseline
describeBy(coplan_0 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##          vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## coplan_0    1 117 3.04 1.59      3    2.97 1.78   1   6     5  0.2    -1.17 0.15
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##          vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## coplan_0    1 123 2.97 1.58    2.8    2.85 1.78   1   6     5  0.4    -0.97 0.14

### Coping planning at 6 months
describeBy(coplan_6 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##          vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## coplan_6    1 88 3.38 1.62    3.4    3.35 2.08   1   6     5 -0.02    -1.17 0.17
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##          vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## coplan_6    1 82 3.37 1.48    3.3    3.38 1.93   1   6     5 -0.04    -1.08 0.16

### Coping planning at 12 months
describeBy(coplan_12 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##           vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## coplan_12    1 85 3.44 1.54    3.8    3.44 1.48   1   6     5 -0.28    -0.97 0.17
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##           vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## coplan_12    1 76 3.48 1.48    3.4    3.47 1.48   1   6     5 -0.06    -0.87 0.17

### Coping planning at 18 months
describeBy(coplan_18 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##           vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## coplan_18    1 79 3.39 1.48    3.2    3.38 1.48   1   6     5 0.06    -0.87 0.17
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##           vars  n mean  sd median trimmed  mad min max range  skew kurtosis   se
## coplan_18    1 70  3.6 1.5    3.8    3.62 1.48   1   6     5 -0.17    -0.82 0.18

### Coping planning at 24 months
describeBy(coplan_24 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##           vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## coplan_24    1 87  3.2 1.47      3    3.15 1.48   1   6     5 0.11    -0.91 0.16
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##           vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## coplan_24    1 82  3.1 1.52    3.1    3.04 1.63   1   6     5 0.15    -1.06 0.17

### Maintenance self-efficacy at baseline
describeBy(aufswe_0 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##          vars   n mean   sd median trimmed  mad min max range  skew kurtosis  se
## aufswe_0    1 118 4.55 1.03   4.67    4.63 0.99   1   6     5 -0.92     1.21 0.1
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##          vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
## aufswe_0    1 123 4.28 1.26   4.33    4.39 0.99   1   6     5 -0.68     0.15 0.11

### Maintenance self-efficacy at 6 months
describeBy(aufswe_6 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##          vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## aufswe_6    1 88 4.34 1.09   4.33    4.39 0.99   1   6     5 -0.44     0.08 0.12
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##          vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## aufswe_6    1 84 4.35 1.11   4.33    4.44 0.99   1   6     5 -0.71     0.63 0.12

### Maintenance self-efficacy at 12 months
describeBy(aufswe_12 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##           vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## aufswe_12    1 86 4.36 0.86      4    4.33 0.99   2   6     4 0.11    -0.17 0.09
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##           vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## aufswe_12    1 76 4.15 1.17   4.33    4.22 0.99   1   6     5 -0.65     0.07 0.13

### Maintenance self-efficacy at 18 months
describeBy(aufswe_18 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##           vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## aufswe_18    1 80 4.27 0.98   4.33     4.3 0.99   1   6     5 -0.5     0.92 0.11
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##           vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## aufswe_18    1 72  4.2 1.23   4.33     4.3 0.99   1   6     5 -0.79     0.46 0.14

### Maintenance self-efficacy at 24 months
describeBy(aufswe_24 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##           vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## aufswe_24    1 87 3.98 1.07      4    4.02 0.99   1   6     5 -0.33     0.09 0.11
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##           vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## aufswe_24    1 83 4.19 1.16   4.33    4.22 0.99   1   6     5 -0.36    -0.13 0.13

### Recovery self-efficacy at baseline
describeBy(wieswe_0 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##          vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
## wieswe_0    1 117 5.13 0.89      5    5.23 1.48   1   6     5 -1.22     2.51 0.08
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##          vars   n mean   sd median trimmed  mad min max range  skew kurtosis  se
## wieswe_0    1 123 4.88 1.09      5    5.04 0.99   1   6     5 -1.45     2.69 0.1

### Recovery self-efficacy at 6 months
describeBy(wieswe_6 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##          vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## wieswe_6    1 87  4.9 1.09      5    5.04 1.48   1   6     5 -1.08     1.21 0.12
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##          vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## wieswe_6    1 84 4.79 1.13      5    4.94 1.48   1   6     5 -1.3     2.18 0.12

### Recovery self-efficacy at 12 months
describeBy(wieswe_12 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##           vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## wieswe_12    1 86  4.8 1.01      5    4.88 1.48   2   6     4 -0.49    -0.27 0.11
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##           vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## wieswe_12    1 76 4.96 0.93      5    5.06 0.99   1   6     5 -1.23     2.71 0.11

### Recovery self-efficacy at 18 months
describeBy(wieswe_18 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##           vars  n mean   sd median trimmed  mad min max range  skew kurtosis  se
## wieswe_18    1 79 4.86 0.91      5    4.91 0.99   1   6     5 -0.87      2.3 0.1
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##           vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## wieswe_18    1 71 4.77 1.22      5    4.95 1.48   1   6     5 -1.25     1.49 0.14

### Recovery self-efficacy at 24 months
describeBy(wieswe_24 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##           vars  n mean   sd median trimmed  mad min max range  skew kurtosis  se
## wieswe_24    1 87 4.54 0.97   4.67    4.59 0.99   1   6     5 -0.53     0.95 0.1
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##           vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## wieswe_24    1 83 4.75 1.12      5    4.88 1.48   1   6     5 -0.92      1.2 0.12

### Action control at baseline
describeBy(hk_0 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##      vars   n mean   sd median trimmed  mad min max range skew kurtosis   se
## hk_0    1 118 3.38 1.39   3.33    3.38 1.61   1   6     5 0.07    -0.88 0.13
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##      vars   n mean   sd median trimmed  mad min max range  skew kurtosis   se
## hk_0    1 123 3.22 1.45   3.33    3.22 1.73   1   6     5 -0.03    -1.14 0.13

### Action control at 6 months
describeBy(hk_6 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##      vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## hk_6    1 87 3.58 1.39    3.5    3.62 1.48   1   6     5 -0.12     -0.8 0.15
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##      vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## hk_6    1 84 3.64 1.33   3.75     3.7 1.36   1   6     5 -0.34    -0.63 0.14

### Action control at 12 months
describeBy(hk_12 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##       vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## hk_12    1 85 3.46 1.31   3.67    3.51 1.24   1   6     5 -0.36    -0.64 0.14
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##       vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## hk_12    1 76 3.78 1.24   3.67    3.82 1.36   1   6     5 -0.2    -0.58 0.14

### Action control at 18 months
describeBy(hk_18 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##       vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## hk_18    1 80 3.58 1.19    3.5     3.6 1.24   1   6     5 -0.17     -0.3 0.13
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##       vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## hk_18    1 71 3.79 1.18   3.83    3.81 1.24   1   6     5 -0.16    -0.46 0.14

### Action control at 24 months
describeBy(hk_24 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##       vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## hk_24    1 87 3.36 1.33   3.33    3.37 1.24   1   6     5 -0.01    -0.65 0.14
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##       vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## hk_24    1 83 3.52 1.34    3.5    3.54 1.48   1   6     5 -0.05    -0.97 0.15

### Collaborative planning at baseline
describeBy(collimpint_0 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##              vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## collimpint_0    1 64 3.29 2.08   3.75    3.25 3.34   1   6     5 0.02    -1.74 0.26
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##              vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## collimpint_0    1 64 3.19 2.12      3    3.12 2.97   1   6     5 0.21    -1.69 0.27

### Collaborative planning at 6 months
describeBy(collimpint_6 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##              vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## collimpint_6    1 52 3.02 2.09   2.88    2.91 2.78   1   6     5 0.21    -1.76 0.29
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##              vars  n mean  sd median trimmed  mad min max range skew kurtosis   se
## collimpint_6    1 43 3.59 2.1      4    3.62 2.97   1   6     5 -0.1    -1.71 0.32

### Collaborative planning at 12 months
describeBy(collimpint_12 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##               vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## collimpint_12    1 44 3.34 2.26   3.38    3.31 3.52   1   6     5 0.07    -1.87 0.34
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##               vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## collimpint_12    1 38  3.9 1.96   4.75    3.98 1.85   1   6     5 -0.36    -1.52 0.32

### Collaborative planning at 18 months
describeBy(collimpint_18 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##               vars  n mean   sd median trimmed  mad min max range  skew kurtosis   se
## collimpint_18    1 51 3.42 2.06    3.5     3.4 3.71   1   6     5 -0.05    -1.69 0.29
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##               vars  n mean   sd median trimmed  mad min max range  skew kurtosis  se
## collimpint_18    1 36 4.09 1.82   4.38     4.2 2.22   1   6     5 -0.55    -1.15 0.3

### Collaborative planning at 24 months
describeBy(collimpint_24 ~ groupfu, data = data)

## 
##  Descriptive statistics by group 
## groupfu: 0
##               vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## collimpint_24    1 44 3.35 2.12   3.75    3.32 3.34   1   6     5 0.01    -1.76 0.32
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##               vars  n mean  sd median trimmed  mad min max range  skew kurtosis   se
## collimpint_24    1 44 3.69 2.1   4.12    3.73 2.78   1   6     5 -0.28    -1.65 0.32

Effect Sizes

### MVPA at baseline
cohen.d(data$mvpaagt_0, data$groupfu)

## Call: cohen.d(x = data$mvpaagt_0, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.33  -0.07  0.19
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.069
## r equivalent of difference between two means
##  data 
## -0.03

### MVPA at 12 months
cohen.d(data$mvpaagt_12, data$groupfu)

## Call: cohen.d(x = data$mvpaagt_12, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.65  -0.33     0
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.33
## r equivalent of difference between two means
##  data 
## -0.16

### MVPA at 24 months
cohen.d(data$mvpaagt_24, data$groupfu)

## Call: cohen.d(x = data$mvpaagt_24, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.47  -0.13  0.21
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.13
## r equivalent of difference between two means
##  data 
## -0.06

### Muscle strength training at baseline
cohen.d(data$muskeltraint_0, data$groupfu) 

## Call: cohen.d(x = data$muskeltraint_0, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.36  -0.11  0.14
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.11
## r equivalent of difference between two means
##  data 
## -0.06

### Muscle strength training at 6 months
cohen.d(data$muskeltraint_6, data$groupfu) 

## Call: cohen.d(x = data$muskeltraint_6, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.09   0.21  0.51
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.21
## r equivalent of difference between two means
## data 
## 0.11

### Muscle strength training at 12 months
cohen.d(data$muskeltraint_12, data$groupfu) 

## Call: cohen.d(x = data$muskeltraint_12, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.04   0.27  0.57
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.27
## r equivalent of difference between two means
## data 
## 0.13

### Muscle strength training at 18 months
cohen.d(data$muskeltraint_18, data$groupfu)

## Call: cohen.d(x = data$muskeltraint_18, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.31   0.01  0.33
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.0091
## r equivalent of difference between two means
## data 
##    0

### Muscle strength training at 24 months
cohen.d(data$muskeltraint_24, data$groupfu) 

## Call: cohen.d(x = data$muskeltraint_24, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.28   0.02  0.32
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.016
## r equivalent of difference between two means
## data 
## 0.01

### Action planning at baseline
cohen.d(data$acplan_0, data$groupfu)

## Call: cohen.d(x = data$acplan_0, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.27  -0.02  0.24
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.017
## r equivalent of difference between two means
##  data 
## -0.01

### Action planning at 6 months
cohen.d(data$acplan_6, data$groupfu)

## Call: cohen.d(x = data$acplan_6, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.01   0.29  0.59
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.29
## r equivalent of difference between two means
## data 
## 0.14

### Action planning at 12 months
cohen.d(data$acplan_12, data$groupfu) 

## Call: cohen.d(x = data$acplan_12, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.04   0.27  0.58
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.27
## r equivalent of difference between two means
## data 
## 0.13

### Action planning at 18 months
cohen.d(data$acplan_18, data$groupfu) 

## Call: cohen.d(x = data$acplan_18, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,]  -0.1   0.22  0.54
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.22
## r equivalent of difference between two means
## data 
## 0.11

### Action planning at 24 months
cohen.d(data$acplan_24, data$groupfu) 

## Call: cohen.d(x = data$acplan_24, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,]  0.04   0.35  0.65
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.35
## r equivalent of difference between two means
## data 
## 0.17

### Coping planning at baseline
cohen.d(data$coplan_0, data$groupfu) 

## Call: cohen.d(x = data$coplan_0, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,]  -0.3  -0.05  0.21
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.048
## r equivalent of difference between two means
##  data 
## -0.02

### Coping planning at 6 months
cohen.d(data$coplan_6, data$groupfu) 

## Call: cohen.d(x = data$coplan_6, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,]  -0.3      0   0.3
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.0035
## r equivalent of difference between two means
## data 
##    0

### Coping planning at 12 months
cohen.d(data$coplan_12, data$groupfu) 

## Call: cohen.d(x = data$coplan_12, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.28   0.03  0.34
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.026
## r equivalent of difference between two means
## data 
## 0.01

### Coping planning at 18 months
cohen.d(data$coplan_18, data$groupfu) 

## Call: cohen.d(x = data$coplan_18, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.19   0.14  0.46
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.14
## r equivalent of difference between two means
## data 
## 0.07

### Coping planning at 24 months
cohen.d(data$coplan_24, data$groupfu) 

## Call: cohen.d(x = data$coplan_24, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.37  -0.07  0.23
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.069
## r equivalent of difference between two means
##  data 
## -0.03

### Maintenance self-efficacy at baseline
cohens_d(data, aufswe_0 ~ groupfu, var.equal = FALSE)

## # A tibble: 1 × 7
##   .y.      group1 group2 effsize    n1    n2 magnitude
## * <chr>    <chr>  <chr>    <dbl> <int> <int> <ord>    
## 1 aufswe_0 0      1        0.230   118   123 small

### Maintenance self-efficacy at 6 months
cohen.d(data$aufswe_6, data$groupfu) 

## Call: cohen.d(x = data$aufswe_6, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.29   0.01  0.31
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.0094
## r equivalent of difference between two means
## data 
##    0

### Maintenance self-efficacy at 12 months
cohens_d(data, aufswe_12 ~ groupfu, var.equal = FALSE)

## # A tibble: 1 × 7
##   .y.       group1 group2 effsize    n1    n2 magnitude 
## * <chr>     <chr>  <chr>    <dbl> <int> <int> <ord>     
## 1 aufswe_12 0      1        0.200    86    76 negligible

### Maintenance self-efficacy at 18 months
cohen.d(data$aufswe_18, data$groupfu) 

## Call: cohen.d(x = data$aufswe_18, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.38  -0.06  0.26
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.058
## r equivalent of difference between two means
##  data 
## -0.03

### Maintenance self-efficacy at 24 months
cohen.d(data$aufswe_24, data$groupfu)

## Call: cohen.d(x = data$aufswe_24, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.11   0.19  0.49
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.19
## r equivalent of difference between two means
## data 
##  0.1

### Recovery self-efficacy at baseline
cohen.d(data$wieswe_0, data$groupfu)

## Call: cohen.d(x = data$wieswe_0, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,]  -0.5  -0.25  0.01
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.25
## r equivalent of difference between two means
##  data 
## -0.12

### Recovery self-efficacy at 6 months
cohen.d(data$wieswe_6, data$groupfu)

## Call: cohen.d(x = data$wieswe_6, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,]  -0.4   -0.1   0.2
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.1
## r equivalent of difference between two means
##  data 
## -0.05

### Recovery self-efficacy at 12 months
cohen.d(data$wieswe_12, data$groupfu)

## Call: cohen.d(x = data$wieswe_12, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.14   0.17  0.48
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.17
## r equivalent of difference between two means
## data 
## 0.08

### Recovery self-efficacy at 18 months
cohen.d(data$wieswe_18, data$groupfu)

## Call: cohen.d(x = data$wieswe_18, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.41  -0.09  0.23
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.09
## r equivalent of difference between two means
##  data 
## -0.04

### Recovery self-efficacy at 24 months
cohen.d(data$wieswe_24, data$groupfu)

## Call: cohen.d(x = data$wieswe_24, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,]  -0.1    0.2   0.5
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.2
## r equivalent of difference between two means
## data 
##  0.1

### Action control at baseline
cohen.d(data$hk_0, data$groupfu) 

## Call: cohen.d(x = data$hk_0, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.37  -0.11  0.14
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.11
## r equivalent of difference between two means
##  data 
## -0.06

### Action control at 6 months
cohen.d(data$hk_6, data$groupfu) 

## Call: cohen.d(x = data$hk_6, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.26   0.04  0.34
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.041
## r equivalent of difference between two means
## data 
## 0.02

### Action control at 12 months
cohen.d(data$hk_12, data$groupfu)

## Call: cohen.d(x = data$hk_12, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.06   0.25  0.56
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.25
## r equivalent of difference between two means
## data 
## 0.12

### Action control at 18 months
cohen.d(data$hk_18, data$groupfu)

## Call: cohen.d(x = data$hk_18, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.14   0.18   0.5
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.18
## r equivalent of difference between two means
## data 
## 0.09

### Action control at 24 months
cohen.d(data$hk_24, data$groupfu)

## Call: cohen.d(x = data$hk_24, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.19   0.12  0.42
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.12
## r equivalent of difference between two means
## data 
## 0.06

### Collaborative planning at baseline
cohen.d(data$collimpint_0, data$groupfu) 

## Call: cohen.d(x = data$collimpint_0, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,]  -0.4  -0.05   0.3
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.051
## r equivalent of difference between two means
##  data 
## -0.03

### Collaborative planning at 6 months
cohen.d(data$collimpint_6, data$groupfu) 

## Call: cohen.d(x = data$collimpint_6, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.13   0.28  0.68
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.28
## r equivalent of difference between two means
## data 
## 0.14

### Collaborative planning at 12 months
cohen.d(data$collimpint_12, data$groupfu) 

## Call: cohen.d(x = data$collimpint_12, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.17   0.27   0.7
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.27
## r equivalent of difference between two means
## data 
## 0.13

### Collaborative planning at 18 months
cohen.d(data$collimpint_18, data$groupfu) 

## Call: cohen.d(x = data$collimpint_18, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.09   0.34  0.77
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.34
## r equivalent of difference between two means
## data 
## 0.17

### Collaborative planning at 24 months
cohen.d(data$collimpint_24, data$groupfu) 

## Call: cohen.d(x = data$collimpint_24, group = data$groupfu)
## Cohen d statistic of difference between two means
##      lower effect upper
## [1,] -0.26   0.16  0.58
## 
## Multivariate (Mahalanobis) distance between groups
## [1] 0.16
## r equivalent of difference between two means
## data 
## 0.08

Between-Group Difference Adjusted for Baseline Indicator

### Outcome: MVPA at Baseline
model <- '
# direct effects
mvpaagt_0 ~ a1*groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 8 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         3
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   mvpaagt_0 ~                                                           
##     groupfu   (a1)   -1.979    3.772   -0.524    0.600   -9.372    5.415
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .mvpaagt_0        48.423    2.685   18.035    0.000   43.161   53.686
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .mvpaagt_0       814.601   76.128   10.700    0.000  665.394  963.809

### Outcome: MVPA at 12 Months
model <- '
# direct effects
mvpaagt_12 ~ a1*groupfu + a2*mvpaagt_0c
# covariances
mvpaagt_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 41 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         4
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   mvpaagt_12 ~                                                          
##     groupfu   (a1)   -3.447    3.112   -1.107    0.268   -9.547    2.653
##     mvpagt_0c (a2)    0.719    0.055   13.185    0.000    0.612    0.826
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     mvpaagt_0c       -0.666    0.937   -0.711    0.477   -2.503    1.171
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .mvpaagt_12       46.835    2.149   21.795    0.000   42.623   51.047
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     mvpaagt_0c       -0.137    1.873   -0.073    0.942   -3.808    3.533
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .mvpaagt_12      342.386   40.363    8.483    0.000  263.276  421.496
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     mvpaagt_0c      812.687   75.589   10.751    0.000  664.535  960.839

### Outcome: MVPA at 24 Months
model <- '
# direct effects
mvpaagt_24 ~ a1*groupfu + a2*mvpaagt_0c
# covariances
mvpaagt_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 47 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         4
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   mvpaagt_24 ~                                                          
##     groupfu   (a1)    3.641    3.084    1.181    0.238   -2.403    9.685
##     mvpagt_0c (a2)    0.730    0.055   13.216    0.000    0.622    0.839
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     mvpaagt_0c       -0.668    0.936   -0.713    0.476   -2.503    1.168
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .mvpaagt_24       36.707    2.184   16.809    0.000   32.427   40.987
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     mvpaagt_0c       -0.124    1.871   -0.066    0.947   -3.792    3.543
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .mvpaagt_24      299.220   37.375    8.006    0.000  225.966  372.475
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     mvpaagt_0c      816.003   75.889   10.753    0.000  667.263  964.742

### Outcome: Muscle Strength Training at Baseline
model <- '
# direct effects
muskeltraint_0 ~ a1*groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         3
## 
##   Number of observations                           241
##   Number of missing patterns                         1
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   muskeltraint_0 ~                                                      
##     groupfu   (a1)   -2.397    2.796   -0.857    0.391   -7.877    3.084
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .muskeltraint_0   17.379    1.998    8.700    0.000   13.464   21.295
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .muskeltraint_0  470.890   42.897   10.977    0.000  386.813  554.966

### Outcome: Muscle Strength Training at 6 Months
model <- '
# direct effects
muskeltraint_6 ~ a1*groupfu + a2*muskeltraint_0c
# covariances
muskeltraint_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model , missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 31 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   muskeltraint_6 ~                                                      
##     groupfu   (a1)    4.724    2.526    1.870    0.061   -0.227    9.675
##     mskltrn_0 (a2)    0.303    0.064    4.744    0.000    0.178    0.429
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     muskeltrant_0c   -1.338    0.943   -1.418    0.156   -3.186    0.511
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .muskeltraint_6   13.990    1.771    7.900    0.000   10.519   17.460
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     muskeltrant_0c   -0.000    1.879   -0.000    1.000   -3.682    3.682
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .muskeltraint_6  273.931   29.453    9.301    0.000  216.204  331.658
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593

### Outcome: Muscle Strength Training at 12 Months
model <- '
# direct effects
muskeltraint_12 ~ a1*groupfu + a2*muskeltraint_0c
# covariances
muskeltraint_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 32 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   muskeltraint_12 ~                                                      
##     groupfu   (a1)     8.308    3.084    2.694    0.007    2.264   14.352
##     mskltrn_0 (a2)     0.554    0.070    7.920    0.000    0.417    0.691
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     muskeltrant_0c   -1.338    0.943   -1.418    0.156   -3.186    0.511
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .muskeltrant_12   16.378    2.112    7.755    0.000   12.238   20.517
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     muskeltrant_0c    0.000    1.879    0.000    1.000   -3.682    3.682
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .muskeltrant_12  383.211   42.448    9.028    0.000  300.014  466.408
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593

### Outcome: Muscle Strength Training at 18 Months
model <- '
# direct effects
muskeltraint_18 ~ a1*groupfu + a2*muskeltraint_0c
# covariances
muskeltraint_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model , missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 37 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   muskeltraint_18 ~                                                      
##     groupfu   (a1)     1.255    2.895    0.433    0.665   -4.419    6.928
##     mskltrn_0 (a2)     0.374    0.067    5.593    0.000    0.243    0.505
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     muskeltrant_0c   -1.338    0.943   -1.418    0.156   -3.186    0.511
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .muskeltrant_18   18.610    1.991    9.345    0.000   14.707   22.514
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     muskeltrant_0c   -0.000    1.879   -0.000    1.000   -3.682    3.682
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .muskeltrant_18  316.138   36.263    8.718    0.000  245.062  387.213
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593

### Outcome: Muscle Strength Training at 24 Months
model <- '
# direct effects
muskeltraint_24 ~ a1*groupfu + a2*muskeltraint_0c
# covariances
muskeltraint_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model , missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 33 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   muskeltraint_24 ~                                                      
##     groupfu   (a1)     3.021    3.736    0.809    0.419   -4.301   10.342
##     mskltrn_0 (a2)     0.521    0.075    6.931    0.000    0.374    0.669
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     muskeltrant_0c   -1.338    0.943   -1.418    0.156   -3.186    0.511
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .muskeltrant_24   21.648    2.606    8.308    0.000   16.540   26.755
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     muskeltrant_0c   -0.000    1.879   -0.000    1.000   -3.682    3.682
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .muskeltrant_24  590.528   63.864    9.247    0.000  465.357  715.700
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593

### Outcome: Action Planning at Baseline
model <- '
# direct effects
acplan_0 ~ a1 * groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 8 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         3
## 
##   Number of observations                           241
##   Number of missing patterns                         1
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   acplan_0 ~                                                            
##     groupfu   (a1)   -0.030    0.227   -0.132    0.895   -0.475    0.415
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_0          3.987    0.162   24.607    0.000    3.670    4.305
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_0          3.098    0.282   10.977    0.000    2.545    3.652

### Outcome: Action Planning at 6 Months
model <- '
# direct effects
acplan_6 ~ a1*groupfu + a2*acplan_0c
# covariances
acplan_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 20 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   acplan_6 ~                                                            
##     groupfu   (a1)    0.538    0.251    2.149    0.032    0.047    1.030
##     acplan_0c (a2)    0.324    0.070    4.598    0.000    0.186    0.462
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     acplan_0c        -0.007    0.057   -0.132    0.895   -0.119    0.104
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_6          4.149    0.177   23.502    0.000    3.803    4.495
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     acplan_0c         0.000    0.113    0.000    1.000   -0.222    0.222
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_6          2.649    0.288    9.192    0.000    2.084    3.214
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     acplan_0c         3.099    0.282   10.977    0.000    2.545    3.652

### Outcome: Action Planning at 12 Months
model <- '
# direct effects
acplan_12 ~ a1*groupfu + a2*acplan_0c
# covariances
acplan_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model , missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 19 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   acplan_12 ~                                                           
##     groupfu   (a1)    0.522    0.243    2.149    0.032    0.046    0.999
##     acplan_0c (a2)    0.384    0.067    5.775    0.000    0.254    0.515
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     acplan_0c        -0.007    0.057   -0.132    0.895   -0.119    0.104
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_12         4.080    0.167   24.475    0.000    3.753    4.407
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     acplan_0c        -0.000    0.113   -0.000    1.000   -0.222    0.222
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_12         2.334    0.262    8.916    0.000    1.821    2.847
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     acplan_0c         3.099    0.282   10.977    0.000    2.545    3.652

### Outcome: Action Planning at 18 Months
model <- '
# direct effects
acplan_18 ~ a1*groupfu + a2*acplan_0c
# covariances
acplan_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model , missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 20 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   acplan_18 ~                                                           
##     groupfu   (a1)    0.380    0.258    1.474    0.140   -0.125    0.886
##     acplan_0c (a2)    0.211    0.073    2.896    0.004    0.068    0.353
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     acplan_0c        -0.007    0.057   -0.132    0.895   -0.119    0.104
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_18         4.218    0.177   23.849    0.000    3.871    4.565
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     acplan_0c         0.000    0.113    0.000    1.000   -0.222    0.222
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_18         2.501    0.288    8.689    0.000    1.937    3.065
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     acplan_0c         3.099    0.282   10.977    0.000    2.545    3.652

### Outcome: Action Planning at 24 Months
model <- '
# direct effects
acplan_24 ~ a1*groupfu + a2*acplan_0c
# covariances
acplan_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 24 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   acplan_24 ~                                                           
##     groupfu   (a1)    0.643    0.258    2.490    0.013    0.137    1.149
##     acplan_0c (a2)    0.340    0.071    4.768    0.000    0.200    0.480
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     acplan_0c        -0.007    0.057   -0.132    0.895   -0.119    0.104
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_24         3.652    0.180   20.246    0.000    3.298    4.005
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     acplan_0c        -0.000    0.113   -0.000    1.000   -0.222    0.222
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_24         2.830    0.307    9.220    0.000    2.228    3.432
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     acplan_0c         3.099    0.282   10.977    0.000    2.545    3.652

### Outcome: Coping Planning at Baseline
model <- '
# direct effects
coplan_0 ~ a1*groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 14 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         3
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   coplan_0 ~                                                            
##     groupfu   (a1)   -0.075    0.204   -0.370    0.711   -0.474    0.324
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_0          3.044    0.146   20.895    0.000    2.759    3.330
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_0          2.484    0.227   10.954    0.000    2.039    2.928

### Outcome: Coping Planning at 6 Months
model <- '
# direct effects
coplan_6 ~ a1*groupfu + a2*coplan_0c
# covariances
coplan_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 23 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         3
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   coplan_6 ~                                                            
##     groupfu   (a1)    0.057    0.216    0.262    0.793   -0.366    0.479
##     coplan_0c (a2)    0.408    0.069    5.912    0.000    0.273    0.543
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     coplan_0c        -0.021    0.051   -0.417    0.677   -0.121    0.079
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_6          3.343    0.150   22.305    0.000    3.049    3.636
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     coplan_0c         0.005    0.102    0.048    0.962   -0.195    0.204
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_6          1.969    0.214    9.195    0.000    1.549    2.388
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     coplan_0c         2.489    0.227   10.941    0.000    2.043    2.935

### Outcome: Coping Planning at 12 Months
model <- '
# direct effects
coplan_12 ~ a1*groupfu + a2*coplan_0c
# covariances
coplan_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 22 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         3
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   coplan_12 ~                                                           
##     groupfu   (a1)    0.094    0.215    0.439    0.661   -0.326    0.515
##     coplan_0c (a2)    0.412    0.068    6.031    0.000    0.278    0.546
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     coplan_0c        -0.020    0.051   -0.385    0.700   -0.119    0.080
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_12         3.415    0.147   23.159    0.000    3.126    3.704
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     coplan_0c         0.002    0.102    0.016    0.987   -0.198    0.201
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_12         1.843    0.205    8.969    0.000    1.440    2.245
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     coplan_0c         2.484    0.227   10.960    0.000    2.040    2.928

### Outcome: Coping Planning at 18 Months
model <- '
# direct effects
coplan_18 ~ a1*groupfu + a2*coplan_0c
# covariances
coplan_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 23 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         3
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   coplan_18 ~                                                           
##     groupfu   (a1)    0.221    0.227    0.975    0.329   -0.223    0.666
##     coplan_0c (a2)    0.339    0.072    4.709    0.000    0.198    0.481
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     coplan_0c        -0.019    0.051   -0.364    0.716   -0.118    0.081
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_18         3.367    0.156   21.639    0.000    3.062    3.672
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     coplan_0c        -0.000    0.102   -0.004    0.996   -0.200    0.199
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_18         1.907    0.221    8.630    0.000    1.474    2.340
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     coplan_0c         2.484    0.227   10.960    0.000    2.040    2.928

### Outcome: Coping Planning at 24 Months
model <- '
# direct effects
coplan_24 ~ a1*groupfu + a2*coplan_0c
# covariances
coplan_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model , missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 22 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         3
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   coplan_24 ~                                                           
##     groupfu   (a1)   -0.095    0.204   -0.466    0.641   -0.496    0.305
##     coplan_0c (a2)    0.430    0.066    6.551    0.000    0.301    0.558
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     coplan_0c        -0.017    0.051   -0.329    0.742   -0.116    0.083
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_24         3.222    0.143   22.610    0.000    2.943    3.502
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     coplan_0c        -0.004    0.102   -0.039    0.969   -0.203    0.195
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_24         1.762    0.192    9.174    0.000    1.385    2.138
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     coplan_0c         2.487    0.227   10.950    0.000    2.042    2.932

### Outcome: Maintenance Self-Efficacy at Baseline
model <- '
# direct effects
aufswe_0 ~ a1*groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         3
## 
##   Number of observations                           241
##   Number of missing patterns                         1
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   aufswe_0 ~                                                            
##     groupfu   (a1)   -0.266    0.149   -1.791    0.073   -0.557    0.025
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_0          4.545    0.106   42.817    0.000    4.337    4.753
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_0          1.330    0.121   10.977    0.000    1.092    1.567

### Outcome: Maintenance Self-Efficacy at 6 Months
model <- '
# direct effects
aufswe_6 ~ a1*groupfu + a2*aufswe_0c
# covariances
aufswe_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model , missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 20 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   aufswe_6 ~                                                            
##     groupfu   (a1)    0.040    0.165    0.244    0.807   -0.283    0.364
##     aufswe_0c (a2)    0.161    0.071    2.264    0.024    0.022    0.301
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     aufswe_0c        -0.066    0.038   -1.767    0.077   -0.140    0.007
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_6          4.324    0.115   37.521    0.000    4.098    4.550
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     aufswe_0c         0.000    0.075    0.000    1.000   -0.147    0.147
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_6          1.164    0.125    9.274    0.000    0.918    1.410
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     aufswe_0c         1.347    0.123   10.977    0.000    1.107    1.588

### Outcome: Maintenance Self-Efficacy at 12 Months
model <- '
# direct effects
aufswe_12 ~ a1*groupfu + a2*aufswe_0c
# covariances
aufswe_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 19 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   aufswe_12 ~                                                           
##     groupfu   (a1)   -0.171    0.159   -1.080    0.280   -0.482    0.139
##     aufswe_0c (a2)    0.133    0.068    1.943    0.052   -0.001    0.267
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     aufswe_0c        -0.066    0.038   -1.767    0.077   -0.140    0.007
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_12         4.350    0.108   40.273    0.000    4.138    4.561
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     aufswe_0c        -0.000    0.075   -0.000    1.000   -0.147    0.147
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_12         1.002    0.111    9.000    0.000    0.784    1.220
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     aufswe_0c         1.347    0.123   10.977    0.000    1.107    1.588

### Outcome: Maintenance Self-Efficacy at 18 Months
model <- '
# direct effects
aufswe_18 ~ a1*groupfu + a2*aufswe_0c
# covariances
aufswe_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model , missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 21 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   aufswe_18 ~                                                           
##     groupfu   (a1)   -0.020    0.171   -0.119    0.905   -0.356    0.315
##     aufswe_0c (a2)    0.263    0.073    3.608    0.000    0.120    0.405
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     aufswe_0c        -0.066    0.038   -1.767    0.077   -0.140    0.007
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_18         4.265    0.117   36.301    0.000    4.034    4.495
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     aufswe_0c         0.000    0.075    0.000    1.000   -0.147    0.147
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_18         1.104    0.127    8.718    0.000    0.856    1.352
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     aufswe_0c         1.347    0.123   10.977    0.000    1.107    1.588

### Outcome: Maintenance Self-Efficacy at 24 Months
model <- '
# direct effects
aufswe_24 ~ a1*groupfu + a2*aufswe_0c
# covariances
aufswe_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model , missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 20 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   aufswe_24 ~                                                           
##     groupfu   (a1)    0.244    0.167    1.456    0.145   -0.084    0.572
##     aufswe_0c (a2)    0.164    0.071    2.308    0.021    0.025    0.302
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     aufswe_0c        -0.066    0.038   -1.767    0.077   -0.140    0.007
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_24         3.974    0.117   34.065    0.000    3.745    4.203
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     aufswe_0c        -0.000    0.075   -0.000    1.000   -0.147    0.147
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_24         1.183    0.128    9.220    0.000    0.932    1.435
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     aufswe_0c         1.347    0.123   10.977    0.000    1.107    1.588

### Outcome: Recovery Self-Efficacy at Baseline
model <- '
# direct effects
wieswe_0 ~ a1*groupfu
' 
model_fit <- sem(data = data, model = model , missing = "FIML")
summary(model_fit, ci = TRUE) 

## lavaan 0.6-18 ended normally after 6 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         3
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wieswe_0 ~                                                            
##     groupfu   (a1)   -0.247    0.129   -1.924    0.054   -0.499    0.005
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_0          5.125    0.092   55.698    0.000    4.945    5.306
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_0          0.991    0.090   10.954    0.000    0.813    1.168

### Outcome: Recovery Self-Efficacy at 6 Months
model <- '
# direct effects
wieswe_6 ~ a1*groupfu + a2*wieswe_0c
# covariances
wieswe_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model , missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 21 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         3
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wieswe_6 ~                                                            
##     groupfu   (a1)   -0.065    0.164   -0.398    0.691   -0.386    0.255
##     wieswe_0c (a2)    0.295    0.083    3.561    0.000    0.132    0.457
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     wieswe_0c        -0.062    0.033   -1.895    0.058   -0.126    0.002
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_6          4.882    0.114   42.706    0.000    4.658    5.106
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     wieswe_0c         0.001    0.065    0.008    0.994   -0.126    0.127
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_6          1.135    0.123    9.247    0.000    0.894    1.375
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     wieswe_0c         1.006    0.092   10.954    0.000    0.826    1.186

### Outcome: Recovery Self-Efficacy at 12 Months
model <- '
# direct effects
wieswe_12 ~ a1*groupfu + a2*wieswe_0c
# covariances
wieswe_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model , missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 17 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         3
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wieswe_12 ~                                                           
##     groupfu   (a1)    0.222    0.148    1.500    0.134   -0.068    0.511
##     wieswe_0c (a2)    0.264    0.072    3.638    0.000    0.122    0.406
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     wieswe_0c        -0.062    0.033   -1.895    0.058   -0.126    0.002
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_12         4.794    0.101   47.651    0.000    4.597    4.991
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     wieswe_0c         0.001    0.065    0.008    0.994   -0.126    0.127
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_12         0.870    0.097    9.000    0.000    0.680    1.059
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     wieswe_0c         1.006    0.092   10.954    0.000    0.826    1.186

### Outcome: Recovery Self-Efficacy at 18 Months
model <- '
# direct effects
wieswe_18 ~ a1*groupfu + a2*wieswe_0c
# covariances
wieswe_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model , missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 23 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         3
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wieswe_18 ~                                                           
##     groupfu   (a1)   -0.069    0.166   -0.414    0.679   -0.394    0.256
##     wieswe_0c (a2)    0.305    0.083    3.661    0.000    0.142    0.469
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     wieswe_0c        -0.062    0.033   -1.895    0.058   -0.126    0.002
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_18         4.870    0.114   42.738    0.000    4.646    5.093
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     wieswe_0c         0.001    0.065    0.008    0.994   -0.126    0.127
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_18         1.026    0.118    8.660    0.000    0.793    1.258
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     wieswe_0c         1.006    0.092   10.954    0.000    0.826    1.186

### Outcome: Recovery Self-Efficacy at 24 Months
model <- '
# direct effects
wieswe_24 ~ a1*groupfu + a2*wieswe_0c
# covariances
wieswe_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 22 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         3
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wieswe_24 ~                                                           
##     groupfu   (a1)    0.249    0.157    1.590    0.112   -0.058    0.557
##     wieswe_0c (a2)    0.211    0.076    2.793    0.005    0.063    0.359
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     wieswe_0c        -0.062    0.033   -1.895    0.058   -0.126    0.002
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_24         4.532    0.109   41.509    0.000    4.318    4.746
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     wieswe_0c         0.001    0.065    0.008    0.994   -0.126    0.127
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_24         1.036    0.112    9.220    0.000    0.816    1.257
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     wieswe_0c         1.006    0.092   10.954    0.000    0.826    1.186

### Outcome: Action Control at Baseline
model <- '
# direct effects
hk_0 ~ a1*groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 1 iteration
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         3
## 
##   Number of observations                           241
##   Number of missing patterns                         1
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   hk_0 ~                                                                
##     groupfu   (a1)   -0.160    0.183   -0.876    0.381   -0.518    0.198
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_0              3.384    0.130   25.938    0.000    3.128    3.640
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_0              2.009    0.183   10.977    0.000    1.650    2.367

### Outcome: Action Control at 6 Months
model <- '
# direct effects
hk_6 ~ a1*groupfu + a2*hk_0c
# covariances
hk_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 21 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   hk_6 ~                                                                
##     groupfu   (a1)    0.083    0.183    0.451    0.652   -0.276    0.441
##     hk_0c     (a2)    0.437    0.064    6.832    0.000    0.312    0.562
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     hk_0c            -0.040    0.046   -0.874    0.382   -0.130    0.050
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_6              3.552    0.128   27.679    0.000    3.300    3.803
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     hk_0c             0.000    0.091    0.000    1.000   -0.179    0.179
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_6              1.431    0.155    9.247    0.000    1.128    1.734
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     hk_0c             2.015    0.184   10.977    0.000    1.655    2.375

### Outcome: Action Control at 12 Months
model <- '
# direct effects
hk_12 ~ a1*groupfu + a2*hk_0c
# covariances
hk_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 20 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   hk_12 ~                                                               
##     groupfu   (a1)    0.345    0.179    1.925    0.054   -0.006    0.697
##     hk_0c     (a2)    0.399    0.064    6.279    0.000    0.274    0.523
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     hk_0c            -0.040    0.046   -0.874    0.382   -0.130    0.050
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_12             3.436    0.123   27.872    0.000    3.194    3.678
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     hk_0c            -0.000    0.091   -0.000    1.000   -0.179    0.179
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_12             1.290    0.144    8.972    0.000    1.008    1.572
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     hk_0c             2.015    0.184   10.977    0.000    1.655    2.375

# a1 p-value: 0.054

### Outcome: Action Control at 18 Months
model <- '
# direct effects
hk_18 ~ a1*groupfu + a2*hk_0c
# covariances
hk_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 20 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   hk_18 ~                                                               
##     groupfu   (a1)    0.243    0.171    1.421    0.155   -0.092    0.577
##     hk_0c     (a2)    0.377    0.060    6.252    0.000    0.259    0.495
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     hk_0c            -0.040    0.046   -0.874    0.382   -0.130    0.050
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_18             3.531    0.117   30.135    0.000    3.302    3.761
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     hk_0c            -0.000    0.091   -0.000    1.000   -0.179    0.179
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_18             1.095    0.126    8.689    0.000    0.848    1.341
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     hk_0c             2.015    0.184   10.977    0.000    1.655    2.375

### Outcome: Action Control at 24 Months
model <- '
# direct effects
hk_24 ~ a1*groupfu + a2*hk_0c
# covariances
hk_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 20 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   hk_24 ~                                                               
##     groupfu   (a1)    0.165    0.184    0.897    0.370   -0.196    0.526
##     hk_0c     (a2)    0.396    0.064    6.149    0.000    0.269    0.522
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     hk_0c            -0.040    0.046   -0.874    0.382   -0.130    0.050
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_24             3.340    0.129   25.946    0.000    3.088    3.593
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     hk_0c             0.000    0.091    0.000    1.000   -0.179    0.179
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_24             1.441    0.156    9.220    0.000    1.135    1.747
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     hk_0c             2.015    0.184   10.977    0.000    1.655    2.375

### Outcome: Collaborative Planning at Baseline
model <- '
# direct effects
collimpint_0 ~ a1*groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 13 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         3
## 
##   Number of observations                           241
##   Number of missing patterns                         2
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   collimpint_0 ~                                                        
##     groupfu   (a1)   -0.105    0.369   -0.286    0.775   -0.829    0.618
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_0      3.293    0.261   12.619    0.000    2.782    3.804
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_0      4.358    0.545    8.000    0.000    3.290    5.426

### Outcome: Collaborative Planning at 6 Months
model <- '
# direct effects
collimpint_6 ~ a1*groupfu + a2*collimpint_0c
# covariances
collimpint_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 26 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         4
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   collimpint_6 ~                                                        
##     groupfu   (a1)    0.473    0.393    1.204    0.229   -0.297    1.244
##     cllmpnt_0 (a2)    0.487    0.103    4.729    0.000    0.285    0.689
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     collimpint_0c    -0.003    0.091   -0.028    0.977   -0.180    0.175
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_6      3.061    0.262   11.666    0.000    2.547    3.575
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     collimpint_0c    -0.040    0.181   -0.218    0.827   -0.394    0.315
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_6      3.296    0.520    6.335    0.000    2.276    4.315
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     collimpint_0c     4.357    0.542    8.038    0.000    3.294    5.419

### Outcome: Collaborative Planning at 12 Months
model <- '
# direct effects
collimpint_12 ~ a1*groupfu + a2*collimpint_0c
# covariances
collimpint_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 28 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         4
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   collimpint_12 ~                                                       
##     groupfu   (a1)    0.423    0.445    0.951    0.341   -0.449    1.295
##     cllmpnt_0 (a2)    0.382    0.119    3.218    0.001    0.149    0.615
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     collimpint_0c    -0.013    0.092   -0.142    0.887   -0.192    0.166
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_12     3.335    0.299   11.148    0.000    2.748    3.921
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     collimpint_0c    -0.053    0.184   -0.291    0.771   -0.413    0.306
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_12     3.783    0.623    6.074    0.000    2.562    5.004
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     collimpint_0c     4.365    0.546    8.001    0.000    3.296    5.435

### Outcome: Collaborative Planning at 18 Months
model <- '
# direct effects
collimpint_18 ~ a1*groupfu + a2*collimpint_0c
# covariances
collimpint_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 27 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         4
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   collimpint_18 ~                                                       
##     groupfu   (a1)    0.368    0.399    0.922    0.357   -0.414    1.150
##     cllmpnt_0 (a2)    0.432    0.101    4.267    0.000    0.234    0.631
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     collimpint_0c    -0.001    0.091   -0.016    0.987   -0.180    0.177
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_18     3.490    0.254   13.718    0.000    2.991    3.988
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     collimpint_0c    -0.099    0.183   -0.538    0.591   -0.458    0.261
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_18     2.949    0.492    5.994    0.000    1.985    3.914
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     collimpint_0c     4.384    0.549    7.986    0.000    3.308    5.459

### Outcome: Collaborative Planning at 24 Months
model <- '
# direct effects
collimpint_24 ~ a1*groupfu + a2*collimpint_0c
# covariances
collimpint_0c ~~ groupfu
' 
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 28 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                         9
## 
##   Number of observations                           241
##   Number of missing patterns                         4
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   collimpint_24 ~                                                       
##     groupfu   (a1)    0.262    0.439    0.596    0.551   -0.599    1.123
##     cllmpnt_0 (a2)    0.228    0.126    1.803    0.071   -0.020    0.475
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     collimpint_0c    -0.027    0.092   -0.289    0.772   -0.207    0.154
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_24     3.334    0.309   10.788    0.000    2.728    3.940
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     collimpint_0c    -0.021    0.184   -0.114    0.909   -0.382    0.340
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_24     4.125    0.637    6.473    0.000    2.876    5.374
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     collimpint_0c     4.363    0.546    7.998    0.000    3.294    5.432

Table 2

Action Planning at 12 Months

model <- '
# Direct Effects
acplan_12c ~ a1*groupfu + a2*acplan_0c + a3*mvpaagt_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
mvpaagt_24 ~ c1*groupfu + c2*acplan_0c + c3*mvpaagt_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*acplan_12c

# Covariances
groupfu ~~ acplan_0c + mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
acplan_0c ~~ mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
mvpaagt_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 418 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        15
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   acplan_12c ~                                                          
##     groupfu   (a1)    0.495    0.240    2.060    0.039    0.024    0.966
##     acpln_0c  (a2)    0.347    0.070    4.930    0.000    0.209    0.485
##     mvpgt_0c  (a3)    0.006    0.005    1.167    0.243   -0.004    0.015
##     HIE       (a4)    0.299    0.291    1.027    0.304   -0.272    0.870
##     LIE       (a5)    0.430    0.295    1.461    0.144   -0.147    1.008
##     sex_0     (a6)    0.304    0.246    1.236    0.216   -0.178    0.786
##     age_0c    (a7)    0.035    0.017    2.035    0.042    0.001    0.069
##     bmi_0c    (a8)   -0.008    0.026   -0.299    0.765   -0.058    0.043
##     panvs_0c  (a9)   -0.031    0.065   -0.480    0.631   -0.158    0.096
##     poshe_0c (a10)   -0.037    0.141   -0.262    0.793   -0.314    0.240
##     swsrNA_0 (a11)   -0.214    0.120   -1.781    0.075   -0.450    0.022
##     fam3_0   (a12)    0.135    0.318    0.425    0.671   -0.487    0.757
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    1.602    2.998    0.534    0.593   -4.274    7.478
##     acpln_0c  (c2)   -0.566    0.937   -0.604    0.546   -2.402    1.270
##     mvpgt_0c  (c3)    0.712    0.059   12.108    0.000    0.596    0.827
##     HIE       (c4)   -3.699    3.514   -1.053    0.292  -10.586    3.188
##     LIE       (c5)    2.943    3.605    0.817    0.414   -4.121   10.008
##     sex_0     (c6)   -1.832    2.943   -0.623    0.533   -7.600    3.935
##     age_0c    (c7)   -0.325    0.224   -1.452    0.147   -0.764    0.114
##     bmi_0c    (c8)   -0.297    0.336   -0.882    0.378   -0.955    0.362
##     panvs_0c  (c9)   -1.895    0.823   -2.302    0.021   -3.508   -0.282
##     poshe_0c (c10)    0.952    1.660    0.574    0.566   -2.301    4.205
##     swsrNA_0 (c11)   -2.134    1.471   -1.451    0.147   -5.017    0.749
##     fam3_0   (c12)    3.991    3.818    1.045    0.296   -3.491   11.474
##     acpln_12  (b1)    2.124    1.020    2.083    0.037    0.125    4.122
## 
## Covariances:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                             
##     acplan_0c         -0.007    0.057   -0.132    0.895   -0.119    0.104
##     mvpaagt_0c        -0.639    0.933   -0.685    0.493   -2.467    1.189
##     HIE                0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE               -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0              0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c            -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c            -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c        -0.151    0.065   -2.312    0.021   -0.280   -0.023
##     poshee_0c          0.003    0.028    0.098    0.922   -0.052    0.058
##     swesourceNA_0c     0.071    0.039    1.850    0.064   -0.004    0.147
##     fam3_0            -0.033    0.013   -2.608    0.009   -0.057   -0.008
##   acplan_0c ~~                                                           
##     mvpaagt_0c        15.774    3.428    4.602    0.000    9.056   22.493
##     HIE                0.038    0.054    0.717    0.473   -0.067    0.144
##     LIE                0.000    0.054    0.004    0.997   -0.105    0.105
##     sex_0             -0.013    0.055   -0.244    0.807   -0.121    0.094
##     age_0c            -0.075    0.862   -0.088    0.930   -1.764    1.613
##     bmi_0c            -1.099    0.556   -1.978    0.048   -2.189   -0.010
##     painvas_0c         0.048    0.228    0.211    0.833   -0.399    0.495
##     poshee_0c          0.255    0.101    2.531    0.011    0.057    0.452
##     swesourceNA_0c    -0.150    0.136   -1.109    0.267   -0.416    0.115
##     fam3_0             0.037    0.044    0.842    0.400   -0.049    0.122
##   mvpaagt_0c ~~                                                          
##     HIE               -0.883    0.885   -0.997    0.319   -2.618    0.852
##     LIE                0.175    0.881    0.198    0.843   -1.552    1.901
##     sex_0              0.839    0.906    0.926    0.354   -0.937    2.615
##     age_0c           -50.649   14.452   -3.505    0.000  -78.974  -22.324
##     bmi_0c           -23.824    9.232   -2.581    0.010  -41.918   -5.730
##     painvas_0c         6.051    3.783    1.600    0.110   -1.363   13.464
##     poshee_0c          2.543    1.635    1.555    0.120   -0.661    5.746
##     swesourceNA_0c     1.442    2.203    0.655    0.513   -2.875    5.760
##     fam3_0             0.274    0.707    0.388    0.698   -1.111    1.659
##   HIE ~~                                                                 
##     LIE               -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0             -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c             0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c             0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c        -0.025    0.061   -0.409    0.683   -0.145    0.095
##     poshee_0c          0.044    0.027    1.630    0.103   -0.009    0.096
##     swesourceNA_0c    -0.033    0.036   -0.914    0.361   -0.104    0.038
##     fam3_0             0.002    0.012    0.181    0.857   -0.021    0.025
##   LIE ~~                                                                 
##     sex_0              0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c            -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c            -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c         0.039    0.062    0.627    0.531   -0.082    0.159
##     poshee_0c         -0.039    0.027   -1.478    0.139   -0.092    0.013
##     swesourceNA_0c     0.008    0.036    0.231    0.817   -0.063    0.079
##     fam3_0            -0.006    0.012   -0.548    0.583   -0.029    0.017
##   sex_0 ~~                                                               
##     age_0c             0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c            -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c        -0.008    0.063   -0.128    0.898   -0.131    0.115
##     poshee_0c         -0.033    0.027   -1.226    0.220   -0.087    0.020
##     swesourceNA_0c    -0.075    0.037   -2.028    0.043   -0.148   -0.003
##     fam3_0            -0.025    0.012   -2.097    0.036   -0.049   -0.002
##   age_0c ~~                                                              
##     bmi_0c            -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c        -2.737    0.995   -2.749    0.006   -4.688   -0.785
##     poshee_0c         -1.304    0.434   -3.004    0.003   -2.155   -0.453
##     swesourceNA_0c    -1.101    0.581   -1.893    0.058   -2.241    0.039
##     fam3_0            -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                              
##     painvas_0c         1.809    0.642    2.817    0.005    0.550    3.067
##     poshee_0c          0.061    0.274    0.223    0.824   -0.476    0.598
##     swesourceNA_0c     0.656    0.375    1.749    0.080   -0.079    1.392
##     fam3_0             0.055    0.121    0.458    0.647   -0.181    0.292
##   painvas_0c ~~                                                          
##     poshee_0c         -0.027    0.120   -0.226    0.821   -0.263    0.208
##     swesourceNA_0c     0.346    0.153    2.255    0.024    0.045    0.647
##     fam3_0             0.067    0.051    1.310    0.190   -0.033    0.166
##   poshee_0c ~~                                                           
##     swesourceNA_0c    -0.124    0.067   -1.863    0.063   -0.255    0.006
##     fam3_0             0.003    0.022    0.136    0.892   -0.039    0.045
##   swesourceNA_0c ~~                                                      
##     fam3_0             0.006    0.029    0.200    0.841   -0.052    0.063
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_12c       -0.652    0.255   -2.554    0.011   -1.152   -0.152
##    .mvpaagt_24       37.143    3.292   11.283    0.000   30.691   43.596
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     acplan_0c        -0.000    0.113   -0.000    1.000   -0.222    0.222
##     mvpaagt_0c       -0.254    1.864   -0.136    0.892   -3.907    3.400
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c            0.000    0.489    0.000    1.000   -0.959    0.959
##     bmi_0c            0.000    0.313    0.000    1.000   -0.614    0.614
##     painvas_0c       -0.005    0.130   -0.035    0.972   -0.259    0.249
##     poshee_0c        -0.000    0.056   -0.005    0.996   -0.110    0.110
##     swesourceNA_0c    0.003    0.077    0.033    0.974   -0.147    0.152
##     fam3_0            0.179    0.025    7.228    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_12c        2.147    0.241    8.907    0.000    1.674    2.619
##    .mvpaagt_24      254.012   31.713    8.010    0.000  191.857  316.168
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     acplan_0c         3.099    0.282   10.977    0.000    2.545    3.652
##     mvpaagt_0c      812.825   75.418   10.778    0.000  665.009  960.642
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.965    0.365   10.864    0.000    3.250    4.680
##     poshee_0c         0.757    0.069   10.958    0.000    0.622    0.892
##     swesourceNA_0c    1.352    0.126   10.725    0.000    1.105    1.600
##     fam3_0            0.147    0.013   10.956    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               1.052    0.716    1.468    0.142   -0.352    2.455
##     total             2.653    2.967    0.894    0.371   -3.161    8.468

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                     est      se     R     2.5%    97.5%
## a1                               0.4951  0.2400 20000   0.0223   0.9630
## a2                               0.3471  0.0711 20000   0.2068   0.4855
## a3                               0.0056  0.0048 20000  -0.0038   0.0149
## a4                               0.2993  0.2915 20000  -0.2645   0.8737
## a5                               0.4304  0.2949 20000  -0.1448   1.0132
## a6                               0.3041  0.2460 20000  -0.1715   0.7896
## a7                               0.0353  0.0172 20000   0.0011   0.0689
## a8                              -0.0077  0.0256 20000  -0.0580   0.0428
## a9                              -0.0312  0.0648 20000  -0.1583   0.0964
## a10                             -0.0370  0.1413 20000  -0.3145   0.2422
## a11                             -0.2141  0.1206 20000  -0.4510   0.0230
## a12                              0.1350  0.3182 20000  -0.4894   0.7653
## c1                               1.6016  3.0170 20000  -4.4066   7.5357
## c2                              -0.5661  0.9397 20000  -2.4138   1.2839
## c3                               0.7115  0.0587 20000   0.5973   0.8260
## c4                              -3.6991  3.4981 20000 -10.5352   3.1681
## c5                               2.9435  3.6127 20000  -4.2100   9.9304
## c6                              -1.8324  2.9515 20000  -7.7044   3.8764
## c7                              -0.3251  0.2248 20000  -0.7590   0.1179
## c8                              -0.2966  0.3367 20000  -0.9557   0.3663
## c9                              -1.8947  0.8282 20000  -3.5246  -0.2750
## c10                              0.9519  1.6550 20000  -2.2742   4.1669
## c11                             -2.1342  1.4709 20000  -4.9865   0.7662
## c12                              3.9914  3.7801 20000  -3.4939  11.4014
## b1                               2.1240  1.0221 20000   0.1287   4.1512
## groupfu~~acplan_0c              -0.0075  0.0565 20000  -0.1190   0.1027
## groupfu~~mvpaagt_0c             -0.6389  0.9307 20000  -2.4535   1.1781
## groupfu~~HIE                     0.0069  0.0152 20000  -0.0228   0.0363
## groupfu~~LIE                    -0.0035  0.0154 20000  -0.0339   0.0264
## groupfu~~sex_0                   0.0003  0.0156 20000  -0.0303   0.0307
## groupfu~~age_0c                 -0.0706  0.2450 20000  -0.5548   0.4093
## groupfu~~bmi_0c                 -0.0398  0.1573 20000  -0.3457   0.2682
## groupfu~~painvas_0c             -0.1513  0.0659 20000  -0.2781  -0.0199
## groupfu~~poshee_0c               0.0028  0.0279 20000  -0.0527   0.0572
## groupfu~~swesourceNA_0c          0.0712  0.0383 20000  -0.0038   0.1460
## groupfu~~fam3_0                 -0.0327  0.0127 20000  -0.0576  -0.0082
## acplan_0c~~mvpaagt_0c           15.7743  3.4161 20000   9.1269  22.5002
## acplan_0c~~HIE                   0.0385  0.0534 20000  -0.0655   0.1426
## acplan_0c~~LIE                   0.0002  0.0537 20000  -0.1047   0.1054
## acplan_0c~~sex_0                -0.0134  0.0547 20000  -0.1207   0.0932
## acplan_0c~~age_0c               -0.0755  0.8625 20000  -1.7510   1.6086
## acplan_0c~~bmi_0c               -1.0993  0.5546 20000  -2.1862  -0.0059
## acplan_0c~~painvas_0c            0.0482  0.2283 20000  -0.4012   0.4932
## acplan_0c~~poshee_0c             0.2545  0.1005 20000   0.0583   0.4512
## acplan_0c~~swesourceNA_0c       -0.1504  0.1361 20000  -0.4195   0.1143
## acplan_0c~~fam3_0                0.0367  0.0437 20000  -0.0492   0.1220
## mvpaagt_0c~~HIE                 -0.8829  0.8849 20000  -2.6178   0.8371
## mvpaagt_0c~~LIE                  0.1747  0.8924 20000  -1.5586   1.9288
## mvpaagt_0c~~sex_0                0.8391  0.9039 20000  -0.9254   2.6058
## mvpaagt_0c~~age_0c             -50.6490 14.4158 20000 -78.8727 -21.8974
## mvpaagt_0c~~bmi_0c             -23.8243  9.2610 20000 -42.1153  -5.8761
## mvpaagt_0c~~painvas_0c           6.0507  3.7688 20000  -1.4637  13.3469
## mvpaagt_0c~~poshee_0c            2.5426  1.6392 20000  -0.7004   5.7236
## mvpaagt_0c~~swesourceNA_0c       1.4421  2.2089 20000  -2.8799   5.7873
## mvpaagt_0c~~fam3_0               0.2740  0.7034 20000  -1.0971   1.6397
## HIE~~LIE                        -0.1144  0.0162 20000  -0.1466  -0.0830
## HIE~~sex_0                      -0.0010  0.0148 20000  -0.0297   0.0280
## HIE~~age_0c                      0.1878  0.2293 20000  -0.2549   0.6397
## HIE~~bmi_0c                      0.3432  0.1505 20000   0.0481   0.6359
## HIE~~painvas_0c                 -0.0250  0.0614 20000  -0.1456   0.0952
## HIE~~poshee_0c                   0.0436  0.0267 20000  -0.0086   0.0961
## HIE~~swesourceNA_0c             -0.0332  0.0364 20000  -0.1050   0.0386
## HIE~~fam3_0                      0.0021  0.0117 20000  -0.0208   0.0254
## LIE~~sex_0                       0.0016  0.0148 20000  -0.0275   0.0305
## LIE~~age_0c                     -0.2255  0.2326 20000  -0.6864   0.2297
## LIE~~bmi_0c                     -0.3705  0.1514 20000  -0.6664  -0.0726
## LIE~~painvas_0c                  0.0386  0.0614 20000  -0.0808   0.1596
## LIE~~poshee_0c                  -0.0395  0.0268 20000  -0.0915   0.0135
## LIE~~swesourceNA_0c              0.0084  0.0362 20000  -0.0622   0.0799
## LIE~~fam3_0                     -0.0064  0.0118 20000  -0.0299   0.0165
## sex_0~~age_0c                    0.0533  0.2361 20000  -0.4092   0.5160
## sex_0~~bmi_0c                   -0.1011  0.1507 20000  -0.3961   0.1915
## sex_0~~painvas_0c               -0.0080  0.0629 20000  -0.1320   0.1152
## sex_0~~poshee_0c                -0.0334  0.0270 20000  -0.0861   0.0194
## sex_0~~swesourceNA_0c           -0.0755  0.0372 20000  -0.1492  -0.0031
## sex_0~~fam3_0                   -0.0253  0.0121 20000  -0.0489  -0.0015
## age_0c~~bmi_0c                  -1.6174  2.3939 20000  -6.3190   3.0640
## age_0c~~painvas_0c              -2.7365  0.9975 20000  -4.7050  -0.7748
## age_0c~~poshee_0c               -1.3043  0.4291 20000  -2.1467  -0.4626
## age_0c~~swesourceNA_0c          -1.1008  0.5824 20000  -2.2546   0.0454
## age_0c~~fam3_0                  -0.0283  0.1884 20000  -0.3986   0.3440
## bmi_0c~~painvas_0c               1.8088  0.6414 20000   0.5478   3.0755
## bmi_0c~~poshee_0c                0.0611  0.2748 20000  -0.4737   0.5985
## bmi_0c~~swesourceNA_0c           0.6564  0.3742 20000  -0.0678   1.3858
## bmi_0c~~fam3_0                   0.0553  0.1201 20000  -0.1775   0.2899
## painvas_0c~~poshee_0c           -0.0272  0.1199 20000  -0.2622   0.2054
## painvas_0c~~swesourceNA_0c       0.3460  0.1545 20000   0.0429   0.6504
## painvas_0c~~fam3_0               0.0665  0.0509 20000  -0.0325   0.1668
## poshee_0c~~swesourceNA_0c       -0.1243  0.0668 20000  -0.2556   0.0060
## poshee_0c~~fam3_0                0.0029  0.0214 20000  -0.0390   0.0449
## swesourceNA_0c~~fam3_0           0.0059  0.0292 20000  -0.0513   0.0635
## acplan_12c~~acplan_12c           2.1468  0.2423 20000   1.6684   2.6183
## mvpaagt_24~~mvpaagt_24         254.0124 31.7728 20000 192.5716 316.7853
## groupfu~~groupfu                 0.2499  0.0227 20000   0.2049   0.2939
## acplan_0c~~acplan_0c             3.0985  0.2812 20000   2.5489   3.6533
## mvpaagt_0c~~mvpaagt_0c         812.8252 75.1781 20000 667.5482 961.2408
## HIE~~HIE                         0.2231  0.0204 20000   0.1838   0.2633
## LIE~~LIE                         0.2245  0.0205 20000   0.1845   0.2645
## sex_0~~sex_0                     0.2340  0.0210 20000   0.1928   0.2755
## age_0c~~age_0c                  57.7334  5.2410 20000  47.5038  68.0048
## bmi_0c~~bmi_0c                  23.6379  2.1582 20000  19.4883  27.9171
## painvas_0c~~painvas_0c           3.9651  0.3655 20000   3.2571   4.6779
## poshee_0c~~poshee_0c             0.7570  0.0687 20000   0.6242   0.8924
## swesourceNA_0c~~swesourceNA_0c   1.3525  0.1265 20000   1.1037   1.6050
## fam3_0~~fam3_0                   0.1470  0.0135 20000   0.1208   0.1737
## acplan_12c~1                    -0.6516  0.2550 20000  -1.1516  -0.1561
## mvpaagt_24~1                    37.1435  3.2904 20000  30.7322  43.6946
## groupfu~1                        0.5104  0.0322 20000   0.4475   0.5736
## acplan_0c~1                      0.0000  0.1133 20000  -0.2247   0.2216
## mvpaagt_0c~1                    -0.2538  1.8729 20000  -3.9445   3.3710
## HIE~1                            0.3361  0.0304 20000   0.2767   0.3965
## LIE~1                            0.3402  0.0304 20000   0.2804   0.4000
## sex_0~1                          0.3734  0.0311 20000   0.3120   0.4335
## age_0c~1                         0.0000  0.4878 20000  -0.9600   0.9410
## bmi_0c~1                         0.0000  0.3159 20000  -0.6150   0.6184
## painvas_0c~1                    -0.0046  0.1285 20000  -0.2567   0.2471
## poshee_0c~1                     -0.0003  0.0563 20000  -0.1088   0.1109
## swesourceNA_0c~1                 0.0025  0.0763 20000  -0.1458   0.1541
## fam3_0~1                         0.1789  0.0251 20000   0.1303   0.2286
## ind                              1.0516  0.7540 20000  -0.0593   2.8025
## total                            2.6531  2.9970 20000  -3.2286   8.5941

Action Planning at 18 months

model <- '
# Direct Effects
acplan_18c ~ a1*groupfu + a2*acplan_0c + a3*mvpaagt_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
mvpaagt_24 ~ c1*groupfu + c2*acplan_0c + c3*mvpaagt_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*acplan_18c

# Covariances
groupfu ~~ acplan_0c + mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
acplan_0c ~~ mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
mvpaagt_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 426 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        15
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   acplan_18c ~                                                          
##     groupfu   (a1)    0.431    0.256    1.682    0.093   -0.071    0.932
##     acpln_0c  (a2)    0.123    0.078    1.579    0.114   -0.030    0.276
##     mvpgt_0c  (a3)    0.009    0.005    1.847    0.065   -0.001    0.019
##     HIE       (a4)    0.274    0.311    0.880    0.379   -0.336    0.883
##     LIE       (a5)    0.117    0.314    0.373    0.709   -0.499    0.733
##     sex_0     (a6)    0.114    0.263    0.432    0.666   -0.402    0.630
##     age_0c    (a7)    0.032    0.019    1.672    0.095   -0.005    0.069
##     bmi_0c    (a8)    0.010    0.028    0.369    0.712   -0.044    0.065
##     panvs_0c  (a9)    0.046    0.072    0.645    0.519   -0.094    0.186
##     poshe_0c (a10)    0.254    0.147    1.724    0.085   -0.035    0.542
##     swsrNA_0 (a11)   -0.186    0.133   -1.397    0.162   -0.447    0.075
##     fam3_0   (a12)    0.275    0.329    0.835    0.403   -0.370    0.919
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    2.539    3.055    0.831    0.406   -3.450    8.527
##     acpln_0c  (c2)   -0.001    0.920   -0.001    0.999   -1.804    1.802
##     mvpgt_0c  (c3)    0.726    0.060   12.173    0.000    0.609    0.843
##     HIE       (c4)   -3.169    3.556   -0.891    0.373  -10.140    3.801
##     LIE       (c5)    3.573    3.640    0.982    0.326   -3.562   10.708
##     sex_0     (c6)   -1.498    2.979   -0.503    0.615   -7.337    4.342
##     age_0c    (c7)   -0.252    0.225   -1.119    0.263   -0.694    0.190
##     bmi_0c    (c8)   -0.252    0.340   -0.742    0.458   -0.918    0.414
##     panvs_0c  (c9)   -1.872    0.836   -2.240    0.025   -3.510   -0.234
##     poshe_0c (c10)    0.496    1.689    0.294    0.769   -2.815    3.807
##     swsrNA_0 (c11)   -2.751    1.466   -1.876    0.061   -5.624    0.123
##     fam3_0   (c12)    4.466    3.871    1.154    0.249   -3.121   12.053
##     acpln_18  (b1)    0.581    1.069    0.544    0.587   -1.514    2.676
## 
## Covariances:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                             
##     acplan_0c         -0.007    0.057   -0.132    0.895   -0.119    0.104
##     mvpaagt_0c        -0.656    0.932   -0.703    0.482   -2.484    1.172
##     HIE                0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE               -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0              0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c            -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c            -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c        -0.151    0.065   -2.309    0.021   -0.279   -0.023
##     poshee_0c          0.003    0.028    0.110    0.912   -0.052    0.058
##     swesourceNA_0c     0.070    0.038    1.807    0.071   -0.006    0.145
##     fam3_0            -0.033    0.013   -2.608    0.009   -0.057   -0.008
##   acplan_0c ~~                                                           
##     mvpaagt_0c        15.711    3.426    4.585    0.000    8.995   22.426
##     HIE                0.038    0.054    0.717    0.473   -0.067    0.144
##     LIE                0.000    0.054    0.004    0.997   -0.105    0.105
##     sex_0             -0.013    0.055   -0.244    0.807   -0.121    0.094
##     age_0c            -0.075    0.862   -0.088    0.930   -1.764    1.613
##     bmi_0c            -1.099    0.556   -1.978    0.048   -2.189   -0.010
##     painvas_0c         0.046    0.228    0.203    0.839   -0.400    0.493
##     poshee_0c          0.252    0.101    2.512    0.012    0.055    0.450
##     swesourceNA_0c    -0.149    0.136   -1.097    0.272   -0.415    0.117
##     fam3_0             0.037    0.044    0.842    0.400   -0.049    0.122
##   mvpaagt_0c ~~                                                          
##     HIE               -0.903    0.885   -1.020    0.308   -2.637    0.831
##     LIE                0.190    0.881    0.216    0.829   -1.536    1.916
##     sex_0              0.824    0.906    0.909    0.363   -0.952    2.599
##     age_0c           -50.805   14.453   -3.515    0.000  -79.132  -22.479
##     bmi_0c           -23.743    9.229   -2.573    0.010  -41.831   -5.654
##     painvas_0c         6.099    3.782    1.613    0.107   -1.314   13.512
##     poshee_0c          2.530    1.634    1.548    0.122   -0.673    5.733
##     swesourceNA_0c     1.509    2.201    0.686    0.493   -2.805    5.823
##     fam3_0             0.274    0.707    0.388    0.698   -1.111    1.660
##   HIE ~~                                                                 
##     LIE               -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0             -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c             0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c             0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c        -0.025    0.061   -0.411    0.681   -0.145    0.095
##     poshee_0c          0.044    0.027    1.647    0.100   -0.008    0.096
##     swesourceNA_0c    -0.034    0.036   -0.938    0.348   -0.105    0.037
##     fam3_0             0.002    0.012    0.181    0.857   -0.021    0.025
##   LIE ~~                                                                 
##     sex_0              0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c            -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c            -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c         0.038    0.062    0.618    0.536   -0.083    0.159
##     poshee_0c         -0.040    0.027   -1.487    0.137   -0.092    0.013
##     swesourceNA_0c     0.009    0.036    0.260    0.795   -0.061    0.080
##     fam3_0            -0.006    0.012   -0.548    0.583   -0.029    0.017
##   sex_0 ~~                                                               
##     age_0c             0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c            -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c        -0.008    0.063   -0.132    0.895   -0.131    0.115
##     poshee_0c         -0.034    0.027   -1.235    0.217   -0.087    0.020
##     swesourceNA_0c    -0.074    0.037   -1.981    0.048   -0.147   -0.001
##     fam3_0            -0.025    0.012   -2.097    0.036   -0.049   -0.002
##   age_0c ~~                                                              
##     bmi_0c            -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c        -2.730    0.995   -2.743    0.006   -4.680   -0.779
##     poshee_0c         -1.303    0.434   -3.001    0.003   -2.153   -0.452
##     swesourceNA_0c    -1.107    0.581   -1.904    0.057   -2.246    0.033
##     fam3_0            -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                              
##     painvas_0c         1.807    0.642    2.816    0.005    0.549    3.066
##     poshee_0c          0.067    0.274    0.244    0.808   -0.470    0.604
##     swesourceNA_0c     0.623    0.375    1.661    0.097   -0.112    1.357
##     fam3_0             0.055    0.121    0.458    0.647   -0.181    0.292
##   painvas_0c ~~                                                          
##     poshee_0c         -0.031    0.120   -0.260    0.795   -0.267    0.204
##     swesourceNA_0c     0.342    0.153    2.233    0.026    0.042    0.643
##     fam3_0             0.066    0.051    1.307    0.191   -0.033    0.166
##   poshee_0c ~~                                                           
##     swesourceNA_0c    -0.127    0.067   -1.899    0.058   -0.257    0.004
##     fam3_0             0.003    0.022    0.131    0.896   -0.039    0.045
##   swesourceNA_0c ~~                                                      
##     fam3_0             0.007    0.029    0.229    0.819   -0.051    0.064
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_18c       -0.447    0.273   -1.638    0.101   -0.982    0.088
##    .mvpaagt_24       36.225    3.344   10.834    0.000   29.671   42.778
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     acplan_0c        -0.000    0.113   -0.000    1.000   -0.222    0.222
##     mvpaagt_0c       -0.256    1.864   -0.137    0.891   -3.909    3.397
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c            0.000    0.489    0.000    1.000   -0.959    0.959
##     bmi_0c           -0.000    0.313   -0.000    1.000   -0.614    0.614
##     painvas_0c       -0.004    0.130   -0.030    0.976   -0.258    0.250
##     poshee_0c         0.000    0.056    0.007    0.995   -0.110    0.110
##     swesourceNA_0c    0.004    0.076    0.053    0.958   -0.146    0.154
##     fam3_0            0.179    0.025    7.228    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_18c        2.331    0.269    8.677    0.000    1.805    2.858
##    .mvpaagt_24      261.612   32.610    8.022    0.000  197.697  325.527
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     acplan_0c         3.099    0.282   10.977    0.000    2.545    3.652
##     mvpaagt_0c      812.728   75.375   10.782    0.000  664.996  960.460
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.964    0.365   10.867    0.000    3.249    4.679
##     poshee_0c         0.757    0.069   10.959    0.000    0.622    0.892
##     swesourceNA_0c    1.351    0.126   10.736    0.000    1.104    1.598
##     fam3_0            0.147    0.013   10.956    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.250    0.490    0.510    0.610   -0.711    1.211
##     total             2.789    2.979    0.936    0.349   -3.051    8.628

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                     est      se     R     2.5%    97.5%
## a1                               0.4306  0.2571 20000  -0.0737   0.9281
## a2                               0.1230  0.0785 20000  -0.0297   0.2764
## a3                               0.0092  0.0050 20000  -0.0005   0.0189
## a4                               0.2736  0.3121 20000  -0.3417   0.8879
## a5                               0.1173  0.3119 20000  -0.5036   0.7251
## a6                               0.1136  0.2623 20000  -0.4031   0.6268
## a7                               0.0317  0.0191 20000  -0.0057   0.0689
## a8                               0.0102  0.0276 20000  -0.0442   0.0647
## a9                               0.0461  0.0712 20000  -0.0943   0.1847
## a10                              0.2535  0.1476 20000  -0.0383   0.5376
## a11                             -0.1860  0.1320 20000  -0.4435   0.0725
## a12                              0.2747  0.3303 20000  -0.3755   0.9213
## c1                               2.5386  3.0627 20000  -3.4164   8.6384
## c2                              -0.0007  0.9269 20000  -1.8220   1.8105
## c3                               0.7260  0.0599 20000   0.6085   0.8430
## c4                              -3.1695  3.5542 20000  -9.9973   3.8979
## c5                               3.5731  3.6462 20000  -3.5642  10.8160
## c6                              -1.4976  2.9685 20000  -7.2694   4.4090
## c7                              -0.2523  0.2255 20000  -0.6927   0.1898
## c8                              -0.2522  0.3405 20000  -0.9163   0.4146
## c9                              -1.8719  0.8375 20000  -3.5141  -0.2491
## c10                              0.4961  1.6848 20000  -2.8085   3.7800
## c11                             -2.7507  1.4613 20000  -5.5919   0.1395
## c12                              4.4663  3.8527 20000  -3.0587  11.9193
## b1                               0.5813  1.0769 20000  -1.5529   2.7174
## groupfu~~acplan_0c              -0.0075  0.0567 20000  -0.1180   0.1035
## groupfu~~mvpaagt_0c             -0.6560  0.9322 20000  -2.4768   1.1692
## groupfu~~HIE                     0.0069  0.0152 20000  -0.0225   0.0365
## groupfu~~LIE                    -0.0035  0.0154 20000  -0.0340   0.0265
## groupfu~~sex_0                   0.0003  0.0156 20000  -0.0301   0.0308
## groupfu~~age_0c                 -0.0706  0.2438 20000  -0.5501   0.4066
## groupfu~~bmi_0c                 -0.0398  0.1569 20000  -0.3510   0.2698
## groupfu~~painvas_0c             -0.1511  0.0658 20000  -0.2795  -0.0216
## groupfu~~poshee_0c               0.0031  0.0278 20000  -0.0506   0.0576
## groupfu~~swesourceNA_0c          0.0695  0.0383 20000  -0.0048   0.1453
## groupfu~~fam3_0                 -0.0327  0.0127 20000  -0.0573  -0.0079
## acplan_0c~~mvpaagt_0c           15.7108  3.3936 20000   8.9538  22.2239
## acplan_0c~~HIE                   0.0385  0.0535 20000  -0.0680   0.1428
## acplan_0c~~LIE                   0.0002  0.0536 20000  -0.1047   0.1047
## acplan_0c~~sex_0                -0.0134  0.0547 20000  -0.1199   0.0946
## acplan_0c~~age_0c               -0.0755  0.8644 20000  -1.7645   1.6252
## acplan_0c~~bmi_0c               -1.0993  0.5554 20000  -2.1938  -0.0062
## acplan_0c~~painvas_0c            0.0463  0.2289 20000  -0.4009   0.4956
## acplan_0c~~poshee_0c             0.2525  0.1005 20000   0.0540   0.4504
## acplan_0c~~swesourceNA_0c       -0.1488  0.1367 20000  -0.4199   0.1153
## acplan_0c~~fam3_0                0.0367  0.0436 20000  -0.0482   0.1219
## mvpaagt_0c~~HIE                 -0.9026  0.8875 20000  -2.6567   0.8087
## mvpaagt_0c~~LIE                  0.1899  0.8843 20000  -1.5499   1.9179
## mvpaagt_0c~~sex_0                0.8236  0.9037 20000  -0.9374   2.5944
## mvpaagt_0c~~age_0c             -50.8055 14.4666 20000 -79.3609 -22.5699
## mvpaagt_0c~~bmi_0c             -23.7425  9.2390 20000 -41.9967  -5.7443
## mvpaagt_0c~~painvas_0c           6.0991  3.7639 20000  -1.2056  13.5913
## mvpaagt_0c~~poshee_0c            2.5297  1.6445 20000  -0.7381   5.7433
## mvpaagt_0c~~swesourceNA_0c       1.5091  2.2106 20000  -2.8356   5.7852
## mvpaagt_0c~~fam3_0               0.2744  0.7033 20000  -1.1050   1.6373
## HIE~~LIE                        -0.1144  0.0163 20000  -0.1460  -0.0825
## HIE~~sex_0                      -0.0010  0.0146 20000  -0.0299   0.0271
## HIE~~age_0c                      0.1878  0.2306 20000  -0.2649   0.6485
## HIE~~bmi_0c                      0.3432  0.1502 20000   0.0482   0.6386
## HIE~~painvas_0c                 -0.0251  0.0614 20000  -0.1438   0.0952
## HIE~~poshee_0c                   0.0440  0.0267 20000  -0.0088   0.0962
## HIE~~swesourceNA_0c             -0.0341  0.0362 20000  -0.1050   0.0362
## HIE~~fam3_0                      0.0021  0.0117 20000  -0.0205   0.0253
## LIE~~sex_0                       0.0016  0.0148 20000  -0.0274   0.0305
## LIE~~age_0c                     -0.2255  0.2320 20000  -0.6771   0.2332
## LIE~~bmi_0c                     -0.3705  0.1508 20000  -0.6704  -0.0743
## LIE~~painvas_0c                  0.0381  0.0622 20000  -0.0839   0.1596
## LIE~~poshee_0c                  -0.0397  0.0266 20000  -0.0920   0.0123
## LIE~~swesourceNA_0c              0.0094  0.0359 20000  -0.0617   0.0797
## LIE~~fam3_0                     -0.0064  0.0118 20000  -0.0296   0.0167
## sex_0~~age_0c                    0.0533  0.2374 20000  -0.4118   0.5259
## sex_0~~bmi_0c                   -0.1011  0.1516 20000  -0.3978   0.1960
## sex_0~~painvas_0c               -0.0083  0.0626 20000  -0.1308   0.1148
## sex_0~~poshee_0c                -0.0336  0.0276 20000  -0.0877   0.0206
## sex_0~~swesourceNA_0c           -0.0737  0.0374 20000  -0.1463  -0.0001
## sex_0~~fam3_0                   -0.0253  0.0121 20000  -0.0490  -0.0015
## age_0c~~bmi_0c                  -1.6174  2.3905 20000  -6.2399   3.0760
## age_0c~~painvas_0c              -2.7297  0.9979 20000  -4.6678  -0.7655
## age_0c~~poshee_0c               -1.3027  0.4310 20000  -2.1486  -0.4703
## age_0c~~swesourceNA_0c          -1.1066  0.5814 20000  -2.2367   0.0437
## age_0c~~fam3_0                  -0.0283  0.1891 20000  -0.4016   0.3373
## bmi_0c~~painvas_0c               1.8075  0.6443 20000   0.5377   3.0593
## bmi_0c~~poshee_0c                0.0668  0.2747 20000  -0.4738   0.6075
## bmi_0c~~swesourceNA_0c           0.6226  0.3752 20000  -0.1043   1.3631
## bmi_0c~~fam3_0                   0.0553  0.1199 20000  -0.1815   0.2890
## painvas_0c~~poshee_0c           -0.0313  0.1204 20000  -0.2651   0.2041
## painvas_0c~~swesourceNA_0c       0.3424  0.1533 20000   0.0440   0.6439
## painvas_0c~~fam3_0               0.0664  0.0514 20000  -0.0342   0.1676
## poshee_0c~~swesourceNA_0c       -0.1266  0.0664 20000  -0.2567   0.0025
## poshee_0c~~fam3_0                0.0028  0.0215 20000  -0.0396   0.0453
## swesourceNA_0c~~fam3_0           0.0067  0.0292 20000  -0.0507   0.0643
## acplan_18c~~acplan_18c           2.3315  0.2677 20000   1.8067   2.8558
## mvpaagt_24~~mvpaagt_24         261.6123 32.6725 20000 198.4656 326.1417
## groupfu~~groupfu                 0.2499  0.0228 20000   0.2051   0.2945
## acplan_0c~~acplan_0c             3.0985  0.2828 20000   2.5453   3.6540
## mvpaagt_0c~~mvpaagt_0c         812.7278 75.1285 20000 667.2058 960.9613
## HIE~~HIE                         0.2231  0.0204 20000   0.1831   0.2627
## LIE~~LIE                         0.2245  0.0206 20000   0.1839   0.2648
## sex_0~~sex_0                     0.2340  0.0210 20000   0.1930   0.2755
## age_0c~~age_0c                  57.7334  5.2399 20000  47.4798  68.0709
## bmi_0c~~bmi_0c                  23.6379  2.1664 20000  19.3982  27.9260
## painvas_0c~~painvas_0c           3.9638  0.3635 20000   3.2516   4.6716
## poshee_0c~~poshee_0c             0.7569  0.0691 20000   0.6204   0.8917
## swesourceNA_0c~~swesourceNA_0c   1.3510  0.1262 20000   1.1042   1.5959
## fam3_0~~fam3_0                   0.1470  0.0135 20000   0.1209   0.1735
## acplan_18c~1                    -0.4472  0.2723 20000  -0.9782   0.0899
## mvpaagt_24~1                    36.2249  3.3362 20000  29.6007  42.7473
## groupfu~1                        0.5104  0.0322 20000   0.4476   0.5737
## acplan_0c~1                      0.0000  0.1130 20000  -0.2198   0.2227
## mvpaagt_0c~1                    -0.2560  1.8709 20000  -3.8786   3.4395
## HIE~1                            0.3361  0.0303 20000   0.2763   0.3964
## LIE~1                            0.3402  0.0305 20000   0.2797   0.4000
## sex_0~1                          0.3734  0.0310 20000   0.3129   0.4331
## age_0c~1                         0.0000  0.4894 20000  -0.9499   0.9609
## bmi_0c~1                         0.0000  0.3157 20000  -0.6209   0.6204
## painvas_0c~1                    -0.0039  0.1291 20000  -0.2579   0.2527
## poshee_0c~1                      0.0004  0.0561 20000  -0.1095   0.1108
## swesourceNA_0c~1                 0.0040  0.0765 20000  -0.1472   0.1513
## fam3_0~1                         0.1789  0.0250 20000   0.1304   0.2289
## ind                              0.2503  0.5663 20000  -0.7506   1.6017
## total                            2.7889  3.0005 20000  -3.0762   8.7664

Coping Planning at 12 Months

model <- '
# Direct Effects
coplan_12c ~ a1*groupfu + a2*coplan_0c + a3*mvpaagt_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
mvpaagt_24 ~ c1*groupfu + c2*coplan_0c + c3*mvpaagt_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*coplan_12c

# Covariances
groupfu ~~ coplan_0c + mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
coplan_0c ~~ mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
mvpaagt_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 417 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        16
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   coplan_12c ~                                                          
##     groupfu   (a1)    0.130    0.215    0.605    0.545   -0.291    0.551
##     copln_0c  (a2)    0.350    0.075    4.670    0.000    0.203    0.497
##     mvpgt_0c  (a3)    0.007    0.004    1.580    0.114   -0.002    0.015
##     HIE       (a4)    0.198    0.259    0.763    0.445   -0.310    0.705
##     LIE       (a5)    0.141    0.263    0.536    0.592   -0.374    0.656
##     sex_0     (a6)    0.032    0.221    0.145    0.885   -0.401    0.465
##     age_0c    (a7)    0.026    0.016    1.575    0.115   -0.006    0.058
##     bmi_0c    (a8)    0.023    0.023    1.006    0.314   -0.022    0.068
##     panvs_0c  (a9)    0.028    0.058    0.477    0.634   -0.087    0.142
##     poshe_0c (a10)    0.237    0.128    1.854    0.064   -0.014    0.487
##     swsrNA_0 (a11)   -0.019    0.108   -0.174    0.862   -0.230    0.193
##     fam3_0   (a12)    0.104    0.278    0.375    0.708   -0.441    0.650
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    2.932    2.929    1.001    0.317   -2.808    8.673
##     copln_0c  (c2)    0.540    1.091    0.495    0.621   -1.598    2.677
##     mvpgt_0c  (c3)    0.709    0.058   12.286    0.000    0.596    0.823
##     HIE       (c4)   -2.811    3.502   -0.803    0.422   -9.676    4.054
##     LIE       (c5)    3.429    3.616    0.948    0.343   -3.658   10.517
##     sex_0     (c6)   -1.198    2.956   -0.405    0.685   -6.991    4.595
##     age_0c    (c7)   -0.399    0.242   -1.647    0.100   -0.873    0.076
##     bmi_0c    (c8)   -0.285    0.339   -0.841    0.400   -0.949    0.379
##     panvs_0c  (c9)   -2.059    0.832   -2.475    0.013   -3.689   -0.429
##     poshe_0c (c10)   -0.172    1.693   -0.102    0.919   -3.489    3.145
##     swsrNA_0 (c11)   -2.453    1.452   -1.690    0.091   -5.299    0.392
##     fam3_0   (c12)    3.521    3.860    0.912    0.362   -4.045   11.087
##     cpln_12c  (b1)    1.799    1.161    1.550    0.121   -0.476    4.074
## 
## Covariances:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                             
##     coplan_0c         -0.019    0.051   -0.377    0.706   -0.119    0.080
##     mvpaagt_0c        -0.714    0.935   -0.764    0.445   -2.546    1.118
##     HIE                0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE               -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0              0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c            -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c            -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c        -0.153    0.066   -2.338    0.019   -0.282   -0.025
##     poshee_0c          0.002    0.028    0.087    0.930   -0.053    0.057
##     swesourceNA_0c     0.070    0.038    1.821    0.069   -0.005    0.146
##     fam3_0            -0.033    0.013   -2.607    0.009   -0.057   -0.008
##   coplan_0c ~~                                                           
##     mvpaagt_0c         5.232    2.973    1.760    0.078   -0.595   11.058
##     HIE               -0.040    0.048   -0.824    0.410   -0.134    0.055
##     LIE                0.039    0.048    0.813    0.416   -0.055    0.134
##     sex_0             -0.091    0.050   -1.841    0.066   -0.188    0.006
##     age_0c             2.385    0.786    3.033    0.002    0.844    3.926
##     bmi_0c            -1.348    0.501   -2.689    0.007   -2.331   -0.365
##     painvas_0c         0.155    0.205    0.756    0.450   -0.247    0.557
##     poshee_0c          0.201    0.090    2.243    0.025    0.025    0.377
##     swesourceNA_0c    -0.134    0.121   -1.104    0.270   -0.372    0.104
##     fam3_0             0.023    0.039    0.587    0.557   -0.053    0.099
##   mvpaagt_0c ~~                                                          
##     HIE               -0.875    0.888   -0.986    0.324   -2.615    0.864
##     LIE                0.164    0.883    0.185    0.853   -1.567    1.894
##     sex_0              0.794    0.908    0.875    0.382   -0.986    2.574
##     age_0c           -51.255   14.488   -3.538    0.000  -79.652  -22.858
##     bmi_0c           -23.982    9.261   -2.590    0.010  -42.134   -5.831
##     painvas_0c         6.304    3.798    1.660    0.097   -1.141   13.748
##     poshee_0c          2.646    1.640    1.613    0.107   -0.569    5.860
##     swesourceNA_0c     1.510    2.209    0.683    0.494   -2.820    5.839
##     fam3_0             0.289    0.708    0.409    0.683   -1.098    1.677
##   HIE ~~                                                                 
##     LIE               -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0             -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c             0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c             0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c        -0.023    0.061   -0.378    0.705   -0.143    0.097
##     poshee_0c          0.043    0.027    1.615    0.106   -0.009    0.096
##     swesourceNA_0c    -0.034    0.036   -0.943    0.346   -0.105    0.037
##     fam3_0             0.002    0.012    0.180    0.857   -0.021    0.025
##   LIE ~~                                                                 
##     sex_0              0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c            -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c            -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c         0.037    0.062    0.601    0.548   -0.084    0.158
##     poshee_0c         -0.039    0.027   -1.471    0.141   -0.092    0.013
##     swesourceNA_0c     0.009    0.036    0.261    0.794   -0.061    0.080
##     fam3_0            -0.006    0.012   -0.546    0.585   -0.029    0.017
##   sex_0 ~~                                                               
##     age_0c             0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c            -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c        -0.007    0.063   -0.113    0.910   -0.130    0.116
##     poshee_0c         -0.033    0.027   -1.217    0.223   -0.087    0.020
##     swesourceNA_0c    -0.075    0.037   -2.007    0.045   -0.148   -0.002
##     fam3_0            -0.025    0.012   -2.098    0.036   -0.049   -0.002
##   age_0c ~~                                                              
##     bmi_0c            -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c        -2.725    0.996   -2.736    0.006   -4.677   -0.773
##     poshee_0c         -1.306    0.434   -3.008    0.003   -2.157   -0.455
##     swesourceNA_0c    -1.097    0.581   -1.886    0.059   -2.236    0.043
##     fam3_0            -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                              
##     painvas_0c         1.835    0.643    2.856    0.004    0.576    3.095
##     poshee_0c          0.056    0.274    0.204    0.838   -0.481    0.593
##     swesourceNA_0c     0.640    0.375    1.708    0.088   -0.095    1.375
##     fam3_0             0.055    0.121    0.460    0.645   -0.181    0.292
##   painvas_0c ~~                                                          
##     poshee_0c         -0.029    0.120   -0.238    0.812   -0.264    0.207
##     swesourceNA_0c     0.342    0.153    2.232    0.026    0.042    0.643
##     fam3_0             0.068    0.051    1.334    0.182   -0.032    0.167
##   poshee_0c ~~                                                           
##     swesourceNA_0c    -0.125    0.067   -1.868    0.062   -0.255    0.006
##     fam3_0             0.003    0.022    0.141    0.888   -0.039    0.045
##   swesourceNA_0c ~~                                                      
##     fam3_0             0.006    0.029    0.197    0.843   -0.052    0.063
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_12c       -0.202    0.228   -0.885    0.376   -0.648    0.245
##    .mvpaagt_24       35.900    3.242   11.075    0.000   29.547   42.254
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     coplan_0c         0.001    0.102    0.008    0.994   -0.198    0.200
##     mvpaagt_0c       -0.341    1.868   -0.183    0.855   -4.003    3.321
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c           -0.000    0.489   -0.000    1.000   -0.959    0.959
##     bmi_0c           -0.000    0.313   -0.000    1.000   -0.614    0.614
##     painvas_0c       -0.004    0.130   -0.035    0.972   -0.259    0.250
##     poshee_0c        -0.001    0.056   -0.016    0.987   -0.111    0.109
##     swesourceNA_0c    0.002    0.076    0.032    0.974   -0.147    0.152
##     fam3_0            0.179    0.025    7.230    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_12c        1.743    0.195    8.960    0.000    1.362    2.125
##    .mvpaagt_24      255.304   31.793    8.030    0.000  192.991  317.617
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     coplan_0c         2.483    0.226   10.964    0.000    2.039    2.926
##     mvpaagt_0c      815.322   75.798   10.757    0.000  666.762  963.883
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.972    0.366   10.848    0.000    3.254    4.690
##     poshee_0c         0.757    0.069   10.957    0.000    0.622    0.892
##     swesourceNA_0c    1.351    0.126   10.739    0.000    1.104    1.597
##     fam3_0            0.147    0.013   10.956    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.234    0.415    0.563    0.573   -0.580    1.048
##     total             3.166    2.954    1.072    0.284   -2.622    8.955

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                     est      se     R     2.5%    97.5%
## a1                               0.1300  0.2143 20000  -0.2882   0.5471
## a2                               0.3500  0.0752 20000   0.2048   0.4976
## a3                               0.0065  0.0041 20000  -0.0016   0.0145
## a4                               0.1976  0.2571 20000  -0.3111   0.7007
## a5                               0.1408  0.2634 20000  -0.3756   0.6588
## a6                               0.0320  0.2207 20000  -0.3993   0.4634
## a7                               0.0257  0.0163 20000  -0.0064   0.0577
## a8                               0.0232  0.0233 20000  -0.0228   0.0685
## a9                               0.0278  0.0583 20000  -0.0860   0.1416
## a10                              0.2369  0.1278 20000  -0.0149   0.4893
## a11                             -0.0188  0.1086 20000  -0.2343   0.1953
## a12                              0.1043  0.2803 20000  -0.4443   0.6529
## c1                               2.9325  2.9443 20000  -2.9095   8.6400
## c2                               0.5396  1.0910 20000  -1.6015   2.6811
## c3                               0.7094  0.0576 20000   0.5971   0.8232
## c4                              -2.8109  3.4917 20000  -9.6657   3.9502
## c5                               3.4292  3.6160 20000  -3.7651  10.4330
## c6                              -1.1985  2.9327 20000  -6.8627   4.6299
## c7                              -0.3986  0.2424 20000  -0.8708   0.0773
## c8                              -0.2850  0.3392 20000  -0.9505   0.3751
## c9                              -2.0586  0.8341 20000  -3.7051  -0.4205
## c10                             -0.1720  1.6793 20000  -3.4281   3.1161
## c11                             -2.4534  1.4557 20000  -5.2852   0.3873
## c12                              3.5209  3.8211 20000  -4.0115  11.0238
## b1                               1.7994  1.1681 20000  -0.4845   4.0851
## groupfu~~coplan_0c              -0.0192  0.0508 20000  -0.1179   0.0810
## groupfu~~mvpaagt_0c             -0.7142  0.9378 20000  -2.5598   1.1178
## groupfu~~HIE                     0.0069  0.0151 20000  -0.0232   0.0366
## groupfu~~LIE                    -0.0035  0.0152 20000  -0.0331   0.0265
## groupfu~~sex_0                   0.0003  0.0155 20000  -0.0299   0.0312
## groupfu~~age_0c                 -0.0706  0.2474 20000  -0.5559   0.4154
## groupfu~~bmi_0c                 -0.0398  0.1571 20000  -0.3489   0.2677
## groupfu~~painvas_0c             -0.1532  0.0660 20000  -0.2816  -0.0236
## groupfu~~poshee_0c               0.0025  0.0280 20000  -0.0528   0.0570
## groupfu~~swesourceNA_0c          0.0701  0.0386 20000  -0.0058   0.1454
## groupfu~~fam3_0                 -0.0327  0.0126 20000  -0.0573  -0.0080
## coplan_0c~~mvpaagt_0c            5.2316  2.9798 20000  -0.5082  11.1385
## coplan_0c~~HIE                  -0.0396  0.0482 20000  -0.1334   0.0557
## coplan_0c~~LIE                   0.0392  0.0483 20000  -0.0543   0.1346
## coplan_0c~~sex_0                -0.0913  0.0497 20000  -0.1901   0.0057
## coplan_0c~~age_0c                2.3852  0.7870 20000   0.8438   3.9207
## coplan_0c~~bmi_0c               -1.3479  0.5043 20000  -2.3370  -0.3583
## coplan_0c~~painvas_0c            0.1551  0.2064 20000  -0.2455   0.5606
## coplan_0c~~poshee_0c             0.2012  0.0899 20000   0.0260   0.3769
## coplan_0c~~swesourceNA_0c       -0.1339  0.1212 20000  -0.3699   0.1039
## coplan_0c~~fam3_0                0.0229  0.0387 20000  -0.0542   0.0985
## mvpaagt_0c~~HIE                 -0.8753  0.8913 20000  -2.6370   0.8643
## mvpaagt_0c~~LIE                  0.1636  0.8851 20000  -1.5800   1.8911
## mvpaagt_0c~~sex_0                0.7942  0.9093 20000  -0.9877   2.5628
## mvpaagt_0c~~age_0c             -51.2547 14.4506 20000 -80.0411 -22.9637
## mvpaagt_0c~~bmi_0c             -23.9821  9.2884 20000 -42.0266  -5.6203
## mvpaagt_0c~~painvas_0c           6.3038  3.7821 20000  -1.0116  13.7884
## mvpaagt_0c~~poshee_0c            2.6458  1.6487 20000  -0.5986   5.8817
## mvpaagt_0c~~swesourceNA_0c       1.5096  2.2233 20000  -2.8567   5.8870
## mvpaagt_0c~~fam3_0               0.2895  0.7053 20000  -1.1029   1.6724
## HIE~~LIE                        -0.1144  0.0160 20000  -0.1452  -0.0824
## HIE~~sex_0                      -0.0010  0.0148 20000  -0.0301   0.0277
## HIE~~age_0c                      0.1878  0.2300 20000  -0.2577   0.6441
## HIE~~bmi_0c                      0.3432  0.1499 20000   0.0518   0.6388
## HIE~~painvas_0c                 -0.0231  0.0614 20000  -0.1426   0.0981
## HIE~~poshee_0c                   0.0431  0.0267 20000  -0.0088   0.0957
## HIE~~swesourceNA_0c             -0.0343  0.0363 20000  -0.1057   0.0372
## HIE~~fam3_0                      0.0021  0.0117 20000  -0.0208   0.0251
## LIE~~sex_0                       0.0016  0.0147 20000  -0.0273   0.0308
## LIE~~age_0c                     -0.2255  0.2334 20000  -0.6881   0.2328
## LIE~~bmi_0c                     -0.3705  0.1509 20000  -0.6662  -0.0748
## LIE~~painvas_0c                  0.0371  0.0622 20000  -0.0858   0.1586
## LIE~~poshee_0c                  -0.0393  0.0267 20000  -0.0919   0.0131
## LIE~~swesourceNA_0c              0.0094  0.0363 20000  -0.0613   0.0811
## LIE~~fam3_0                     -0.0064  0.0117 20000  -0.0292   0.0165
## sex_0~~age_0c                    0.0533  0.2384 20000  -0.4135   0.5268
## sex_0~~bmi_0c                   -0.1011  0.1503 20000  -0.3991   0.1937
## sex_0~~painvas_0c               -0.0071  0.0624 20000  -0.1306   0.1150
## sex_0~~poshee_0c                -0.0331  0.0271 20000  -0.0868   0.0193
## sex_0~~swesourceNA_0c           -0.0747  0.0372 20000  -0.1478  -0.0019
## sex_0~~fam3_0                   -0.0253  0.0121 20000  -0.0489  -0.0018
## age_0c~~bmi_0c                  -1.6174  2.4062 20000  -6.3206   3.1886
## age_0c~~painvas_0c              -2.7251  0.9990 20000  -4.6603  -0.7775
## age_0c~~poshee_0c               -1.3058  0.4335 20000  -2.1496  -0.4566
## age_0c~~swesourceNA_0c          -1.0966  0.5831 20000  -2.2380   0.0561
## age_0c~~fam3_0                  -0.0283  0.1883 20000  -0.3988   0.3363
## bmi_0c~~painvas_0c               1.8353  0.6450 20000   0.5721   3.1017
## bmi_0c~~poshee_0c                0.0559  0.2731 20000  -0.4784   0.5932
## bmi_0c~~swesourceNA_0c           0.6402  0.3730 20000  -0.0878   1.3808
## bmi_0c~~fam3_0                   0.0555  0.1210 20000  -0.1835   0.2915
## painvas_0c~~poshee_0c           -0.0286  0.1203 20000  -0.2599   0.2119
## painvas_0c~~swesourceNA_0c       0.3425  0.1534 20000   0.0421   0.6426
## painvas_0c~~fam3_0               0.0678  0.0512 20000  -0.0330   0.1677
## poshee_0c~~swesourceNA_0c       -0.1246  0.0670 20000  -0.2551   0.0060
## poshee_0c~~fam3_0                0.0030  0.0214 20000  -0.0391   0.0455
## swesourceNA_0c~~fam3_0           0.0058  0.0294 20000  -0.0510   0.0640
## coplan_12c~~coplan_12c           1.7432  0.1952 20000   1.3581   2.1279
## mvpaagt_24~~mvpaagt_24         255.3037 31.8528 20000 193.6809 318.2818
## groupfu~~groupfu                 0.2499  0.0228 20000   0.2050   0.2944
## coplan_0c~~coplan_0c             2.4826  0.2258 20000   2.0427   2.9241
## mvpaagt_0c~~mvpaagt_0c         815.3223 75.5572 20000 666.0951 961.3095
## HIE~~HIE                         0.2231  0.0204 20000   0.1835   0.2633
## LIE~~LIE                         0.2245  0.0205 20000   0.1847   0.2641
## sex_0~~sex_0                     0.2340  0.0211 20000   0.1928   0.2761
## age_0c~~age_0c                  57.7334  5.2398 20000  47.4097  67.9530
## bmi_0c~~bmi_0c                  23.6379  2.1590 20000  19.4801  27.9278
## painvas_0c~~painvas_0c           3.9720  0.3651 20000   3.2529   4.6826
## poshee_0c~~poshee_0c             0.7571  0.0692 20000   0.6217   0.8936
## swesourceNA_0c~~swesourceNA_0c   1.3505  0.1242 20000   1.1072   1.5923
## fam3_0~~fam3_0                   0.1470  0.0135 20000   0.1208   0.1736
## coplan_12c~1                    -0.2016  0.2271 20000  -0.6507   0.2423
## mvpaagt_24~1                    35.9004  3.2477 20000  29.5216  42.3158
## groupfu~1                        0.5104  0.0321 20000   0.4464   0.5731
## coplan_0c~1                      0.0008  0.1022 20000  -0.1990   0.2019
## mvpaagt_0c~1                    -0.3413  1.8769 20000  -4.0440   3.2919
## HIE~1                            0.3361  0.0302 20000   0.2767   0.3954
## LIE~1                            0.3402  0.0304 20000   0.2800   0.3999
## sex_0~1                          0.3734  0.0311 20000   0.3128   0.4340
## age_0c~1                         0.0000  0.4868 20000  -0.9442   0.9601
## bmi_0c~1                         0.0000  0.3152 20000  -0.6124   0.6126
## painvas_0c~1                    -0.0045  0.1308 20000  -0.2593   0.2527
## poshee_0c~1                     -0.0009  0.0560 20000  -0.1110   0.1079
## swesourceNA_0c~1                 0.0025  0.0760 20000  -0.1449   0.1532
## fam3_0~1                         0.1789  0.0248 20000   0.1304   0.2273
## ind                              0.2339  0.4841 20000  -0.6125   1.3710
## total                            3.1664  2.9785 20000  -2.7605   8.9769

Coping Planning at 18 Months

model <- '
# Direct Effects
coplan_18c ~ a1*groupfu + a2*coplan_0c + a3*mvpaagt_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
mvpaagt_24 ~ c1*groupfu + c2*coplan_0c + c3*mvpaagt_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*coplan_18c

# Covariances
groupfu ~~ coplan_0c + mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
coplan_0c ~~ mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
mvpaagt_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 420 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        16
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   coplan_18c ~                                                          
##     groupfu   (a1)    0.330    0.213    1.547    0.122   -0.088    0.748
##     copln_0c  (a2)    0.263    0.074    3.569    0.000    0.119    0.408
##     mvpgt_0c  (a3)    0.003    0.004    0.868    0.385   -0.004    0.011
##     HIE       (a4)    0.557    0.257    2.169    0.030    0.054    1.060
##     LIE       (a5)    0.212    0.263    0.807    0.420   -0.304    0.728
##     sex_0     (a6)   -0.350    0.221   -1.582    0.114   -0.784    0.084
##     age_0c    (a7)    0.031    0.016    1.917    0.055   -0.001    0.064
##     bmi_0c    (a8)    0.066    0.023    2.847    0.004    0.021    0.111
##     panvs_0c  (a9)    0.171    0.060    2.847    0.004    0.053    0.288
##     poshe_0c (a10)    0.202    0.124    1.620    0.105   -0.042    0.446
##     swsrNA_0 (a11)   -0.122    0.111   -1.103    0.270   -0.339    0.095
##     fam3_0   (a12)    0.023    0.273    0.083    0.933   -0.512    0.558
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    2.527    3.010    0.839    0.401   -3.373    8.426
##     copln_0c  (c2)    0.812    1.107    0.734    0.463   -1.357    2.981
##     mvpgt_0c  (c3)    0.716    0.058   12.348    0.000    0.603    0.830
##     HIE       (c4)   -3.266    3.585   -0.911    0.362  -10.293    3.760
##     LIE       (c5)    3.090    3.651    0.846    0.397   -4.067   10.247
##     sex_0     (c6)   -0.911    2.989   -0.305    0.761   -6.770    4.948
##     age_0c    (c7)   -0.351    0.243   -1.447    0.148   -0.827    0.125
##     bmi_0c    (c8)   -0.289    0.353   -0.821    0.412   -0.981    0.402
##     panvs_0c  (c9)   -2.121    0.861   -2.463    0.014   -3.809   -0.433
##     poshe_0c (c10)    0.055    1.701    0.032    0.974   -3.279    3.389
##     swsrNA_0 (c11)   -2.512    1.473   -1.705    0.088   -5.400    0.375
##     fam3_0   (c12)    4.005    3.869    1.035    0.301   -3.578   11.588
##     cpln_18c  (b1)    0.952    1.308    0.728    0.466   -1.610    3.515
## 
## Covariances:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                             
##     coplan_0c         -0.019    0.051   -0.369    0.712   -0.118    0.081
##     mvpaagt_0c        -0.731    0.935   -0.782    0.434   -2.563    1.101
##     HIE                0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE               -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0              0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c            -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c            -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c        -0.152    0.065   -2.327    0.020   -0.281   -0.024
##     poshee_0c          0.003    0.028    0.116    0.908   -0.052    0.058
##     swesourceNA_0c     0.069    0.038    1.787    0.074   -0.007    0.144
##     fam3_0            -0.033    0.013   -2.607    0.009   -0.057   -0.008
##   coplan_0c ~~                                                           
##     mvpaagt_0c         5.247    2.973    1.765    0.078   -0.579   11.074
##     HIE               -0.039    0.048   -0.818    0.413   -0.133    0.055
##     LIE                0.039    0.048    0.801    0.423   -0.056    0.133
##     sex_0             -0.092    0.050   -1.851    0.064   -0.189    0.005
##     age_0c             2.385    0.786    3.033    0.002    0.844    3.926
##     bmi_0c            -1.345    0.501   -2.683    0.007   -2.328   -0.363
##     painvas_0c         0.163    0.205    0.793    0.428   -0.239    0.564
##     poshee_0c          0.197    0.090    2.198    0.028    0.021    0.373
##     swesourceNA_0c    -0.131    0.121   -1.078    0.281   -0.368    0.107
##     fam3_0             0.023    0.039    0.591    0.555   -0.053    0.099
##   mvpaagt_0c ~~                                                          
##     HIE               -0.861    0.887   -0.971    0.332   -2.600    0.877
##     LIE                0.159    0.883    0.180    0.857   -1.571    1.888
##     sex_0              0.782    0.908    0.861    0.389   -0.998    2.561
##     age_0c           -51.337   14.487   -3.544    0.000  -79.731  -22.944
##     bmi_0c           -23.713    9.255   -2.562    0.010  -41.853   -5.572
##     painvas_0c         6.475    3.796    1.706    0.088   -0.965   13.916
##     poshee_0c          2.632    1.640    1.605    0.108   -0.582    5.845
##     swesourceNA_0c     1.531    2.208    0.693    0.488   -2.796    5.857
##     fam3_0             0.285    0.708    0.402    0.688   -1.103    1.672
##   HIE ~~                                                                 
##     LIE               -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0             -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c             0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c             0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c        -0.024    0.061   -0.389    0.697   -0.143    0.096
##     poshee_0c          0.044    0.027    1.655    0.098   -0.008    0.097
##     swesourceNA_0c    -0.036    0.036   -0.980    0.327   -0.107    0.036
##     fam3_0             0.002    0.012    0.180    0.857   -0.021    0.025
##   LIE ~~                                                                 
##     sex_0              0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c            -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c            -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c         0.037    0.062    0.599    0.549   -0.084    0.158
##     poshee_0c         -0.040    0.027   -1.491    0.136   -0.092    0.013
##     swesourceNA_0c     0.010    0.036    0.279    0.780   -0.061    0.081
##     fam3_0            -0.006    0.012   -0.546    0.585   -0.029    0.017
##   sex_0 ~~                                                               
##     age_0c             0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c            -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c        -0.008    0.063   -0.123    0.902   -0.130    0.115
##     poshee_0c         -0.034    0.027   -1.240    0.215   -0.087    0.020
##     swesourceNA_0c    -0.074    0.037   -1.987    0.047   -0.147   -0.001
##     fam3_0            -0.025    0.012   -2.098    0.036   -0.049   -0.002
##   age_0c ~~                                                              
##     bmi_0c            -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c        -2.715    0.996   -2.727    0.006   -4.666   -0.764
##     poshee_0c         -1.302    0.434   -2.999    0.003   -2.153   -0.451
##     swesourceNA_0c    -1.102    0.581   -1.897    0.058   -2.241    0.037
##     fam3_0            -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                              
##     painvas_0c         1.830    0.642    2.849    0.004    0.571    3.089
##     poshee_0c          0.070    0.274    0.254    0.800   -0.468    0.607
##     swesourceNA_0c     0.626    0.375    1.670    0.095   -0.108    1.360
##     fam3_0             0.055    0.121    0.460    0.645   -0.181    0.292
##   painvas_0c ~~                                                          
##     poshee_0c         -0.031    0.120   -0.258    0.797   -0.265    0.204
##     swesourceNA_0c     0.339    0.153    2.212    0.027    0.039    0.640
##     fam3_0             0.068    0.051    1.332    0.183   -0.032    0.167
##   poshee_0c ~~                                                           
##     swesourceNA_0c    -0.124    0.067   -1.863    0.062   -0.255    0.006
##     fam3_0             0.003    0.022    0.127    0.899   -0.039    0.045
##   swesourceNA_0c ~~                                                      
##     fam3_0             0.006    0.029    0.222    0.825   -0.051    0.064
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_18c       -0.310    0.227   -1.368    0.171   -0.754    0.134
##    .mvpaagt_24       36.378    3.304   11.012    0.000   29.903   42.853
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     coplan_0c        -0.000    0.102   -0.000    1.000   -0.199    0.199
##     mvpaagt_0c       -0.315    1.868   -0.169    0.866   -3.976    3.346
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c           -0.000    0.489   -0.000    1.000   -0.959    0.959
##     bmi_0c           -0.000    0.313   -0.000    1.000   -0.614    0.614
##     painvas_0c       -0.003    0.130   -0.025    0.980   -0.257    0.251
##     poshee_0c         0.001    0.056    0.013    0.990   -0.109    0.111
##     swesourceNA_0c    0.000    0.076    0.005    0.996   -0.150    0.150
##     fam3_0            0.179    0.025    7.230    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_18c        1.587    0.184    8.611    0.000    1.226    1.949
##    .mvpaagt_24      258.790   32.260    8.022    0.000  195.562  322.019
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     coplan_0c         2.483    0.226   10.964    0.000    2.039    2.927
##     mvpaagt_0c      814.947   75.714   10.764    0.000  666.551  963.343
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.971    0.366   10.854    0.000    3.254    4.688
##     poshee_0c         0.757    0.069   10.958    0.000    0.622    0.892
##     swesourceNA_0c    1.349    0.126   10.748    0.000    1.103    1.595
##     fam3_0            0.147    0.013   10.956    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.314    0.486    0.647    0.518   -0.638    1.267
##     total             2.841    2.957    0.961    0.337   -2.955    8.637

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                     est      se     R     2.5%    97.5%
## a1                               0.3300  0.2149 20000  -0.0910   0.7559
## a2                               0.2633  0.0742 20000   0.1163   0.4079
## a3                               0.0035  0.0040 20000  -0.0043   0.0113
## a4                               0.5566  0.2572 20000   0.0580   1.0672
## a5                               0.2124  0.2633 20000  -0.2984   0.7367
## a6                              -0.3500  0.2219 20000  -0.7815   0.0856
## a7                               0.0315  0.0165 20000  -0.0009   0.0636
## a8                               0.0659  0.0234 20000   0.0203   0.1123
## a9                               0.1705  0.0596 20000   0.0533   0.2865
## a10                              0.2016  0.1254 20000  -0.0411   0.4497
## a11                             -0.1222  0.1108 20000  -0.3355   0.0986
## a12                              0.0228  0.2750 20000  -0.5183   0.5567
## c1                               2.5266  3.0028 20000  -3.3426   8.5042
## c2                               0.8119  1.1089 20000  -1.3333   2.9789
## c3                               0.7163  0.0582 20000   0.6014   0.8309
## c4                              -3.2664  3.5834 20000 -10.2276   3.8633
## c5                               3.0902  3.6614 20000  -3.9892  10.2969
## c6                              -0.9109  2.9759 20000  -6.6632   4.9989
## c7                              -0.3514  0.2420 20000  -0.8195   0.1244
## c8                              -0.2894  0.3510 20000  -0.9816   0.3955
## c9                              -2.1213  0.8573 20000  -3.7984  -0.4570
## c10                              0.0547  1.6937 20000  -3.2823   3.3215
## c11                             -2.5121  1.4659 20000  -5.3984   0.3536
## c12                              4.0049  3.8291 20000  -3.4651  11.5055
## b1                               0.9525  1.3116 20000  -1.6453   3.5218
## groupfu~~coplan_0c              -0.0187  0.0508 20000  -0.1183   0.0797
## groupfu~~mvpaagt_0c             -0.7308  0.9352 20000  -2.5722   1.0888
## groupfu~~HIE                     0.0069  0.0152 20000  -0.0227   0.0371
## groupfu~~LIE                    -0.0035  0.0151 20000  -0.0333   0.0263
## groupfu~~sex_0                   0.0003  0.0154 20000  -0.0303   0.0305
## groupfu~~age_0c                 -0.0706  0.2463 20000  -0.5524   0.4075
## groupfu~~bmi_0c                 -0.0398  0.1561 20000  -0.3469   0.2634
## groupfu~~painvas_0c             -0.1524  0.0653 20000  -0.2792  -0.0241
## groupfu~~poshee_0c               0.0033  0.0279 20000  -0.0516   0.0576
## groupfu~~swesourceNA_0c          0.0687  0.0386 20000  -0.0064   0.1450
## groupfu~~fam3_0                 -0.0327  0.0126 20000  -0.0573  -0.0080
## coplan_0c~~mvpaagt_0c            5.2471  2.9846 20000  -0.5343  11.2474
## coplan_0c~~HIE                  -0.0393  0.0484 20000  -0.1342   0.0554
## coplan_0c~~LIE                   0.0387  0.0486 20000  -0.0585   0.1323
## coplan_0c~~sex_0                -0.0918  0.0493 20000  -0.1873   0.0061
## coplan_0c~~age_0c                2.3849  0.7857 20000   0.8575   3.9263
## coplan_0c~~bmi_0c               -1.3454  0.4999 20000  -2.3262  -0.3606
## coplan_0c~~painvas_0c            0.1626  0.2056 20000  -0.2338   0.5692
## coplan_0c~~poshee_0c             0.1971  0.0892 20000   0.0210   0.3716
## coplan_0c~~swesourceNA_0c       -0.1306  0.1209 20000  -0.3661   0.1078
## coplan_0c~~fam3_0                0.0230  0.0390 20000  -0.0526   0.0998
## mvpaagt_0c~~HIE                 -0.8615  0.8912 20000  -2.6066   0.8550
## mvpaagt_0c~~LIE                  0.1586  0.8803 20000  -1.5664   1.8540
## mvpaagt_0c~~sex_0                0.7818  0.9057 20000  -1.0047   2.5561
## mvpaagt_0c~~age_0c             -51.3373 14.4561 20000 -79.6601 -22.6121
## mvpaagt_0c~~bmi_0c             -23.7126  9.2490 20000 -41.7109  -5.5447
## mvpaagt_0c~~painvas_0c           6.4754  3.7598 20000  -0.9276  13.9378
## mvpaagt_0c~~poshee_0c            2.6316  1.6503 20000  -0.6118   5.8605
## mvpaagt_0c~~swesourceNA_0c       1.5306  2.2166 20000  -2.8128   5.8217
## mvpaagt_0c~~fam3_0               0.2847  0.7042 20000  -1.0921   1.6613
## HIE~~LIE                        -0.1144  0.0162 20000  -0.1461  -0.0825
## HIE~~sex_0                      -0.0010  0.0148 20000  -0.0300   0.0282
## HIE~~age_0c                      0.1878  0.2292 20000  -0.2593   0.6339
## HIE~~bmi_0c                      0.3432  0.1499 20000   0.0485   0.6370
## HIE~~painvas_0c                 -0.0237  0.0613 20000  -0.1446   0.0971
## HIE~~poshee_0c                   0.0442  0.0267 20000  -0.0083   0.0967
## HIE~~swesourceNA_0c             -0.0356  0.0364 20000  -0.1073   0.0355
## HIE~~fam3_0                      0.0021  0.0117 20000  -0.0208   0.0250
## LIE~~sex_0                       0.0016  0.0147 20000  -0.0273   0.0303
## LIE~~age_0c                     -0.2255  0.2331 20000  -0.6824   0.2338
## LIE~~bmi_0c                     -0.3705  0.1504 20000  -0.6660  -0.0759
## LIE~~painvas_0c                  0.0369  0.0616 20000  -0.0836   0.1574
## LIE~~poshee_0c                  -0.0398  0.0266 20000  -0.0914   0.0127
## LIE~~swesourceNA_0c              0.0101  0.0362 20000  -0.0604   0.0819
## LIE~~fam3_0                     -0.0064  0.0118 20000  -0.0296   0.0165
## sex_0~~age_0c                    0.0533  0.2362 20000  -0.4069   0.5205
## sex_0~~bmi_0c                   -0.1011  0.1518 20000  -0.4004   0.1976
## sex_0~~painvas_0c               -0.0077  0.0630 20000  -0.1309   0.1175
## sex_0~~poshee_0c                -0.0338  0.0272 20000  -0.0865   0.0194
## sex_0~~swesourceNA_0c           -0.0739  0.0370 20000  -0.1460  -0.0012
## sex_0~~fam3_0                   -0.0253  0.0122 20000  -0.0491  -0.0015
## age_0c~~bmi_0c                  -1.6174  2.3987 20000  -6.2861   3.0911
## age_0c~~painvas_0c              -2.7147  0.9994 20000  -4.6602  -0.7469
## age_0c~~poshee_0c               -1.3019  0.4362 20000  -2.1622  -0.4502
## age_0c~~swesourceNA_0c          -1.1023  0.5845 20000  -2.2566   0.0435
## age_0c~~fam3_0                  -0.0283  0.1896 20000  -0.4029   0.3379
## bmi_0c~~painvas_0c               1.8298  0.6410 20000   0.5582   3.0762
## bmi_0c~~poshee_0c                0.0696  0.2725 20000  -0.4656   0.6047
## bmi_0c~~swesourceNA_0c           0.6258  0.3706 20000  -0.0976   1.3539
## bmi_0c~~fam3_0                   0.0555  0.1199 20000  -0.1823   0.2907
## painvas_0c~~poshee_0c           -0.0308  0.1192 20000  -0.2618   0.2061
## painvas_0c~~swesourceNA_0c       0.3391  0.1533 20000   0.0418   0.6372
## painvas_0c~~fam3_0               0.0676  0.0511 20000  -0.0326   0.1674
## poshee_0c~~swesourceNA_0c       -0.1242  0.0669 20000  -0.2541   0.0075
## poshee_0c~~fam3_0                0.0027  0.0214 20000  -0.0396   0.0443
## swesourceNA_0c~~fam3_0           0.0065  0.0292 20000  -0.0506   0.0634
## coplan_18c~~coplan_18c           1.5874  0.1862 20000   1.2246   1.9559
## mvpaagt_24~~mvpaagt_24         258.7905 32.3209 20000 196.3086 322.6699
## groupfu~~groupfu                 0.2499  0.0227 20000   0.2058   0.2947
## coplan_0c~~coplan_0c             2.4828  0.2253 20000   2.0419   2.9221
## mvpaagt_0c~~mvpaagt_0c         814.9471 75.4777 20000 669.1257 963.7780
## HIE~~HIE                         0.2231  0.0203 20000   0.1835   0.2633
## LIE~~LIE                         0.2245  0.0204 20000   0.1850   0.2644
## sex_0~~sex_0                     0.2340  0.0212 20000   0.1927   0.2752
## age_0c~~age_0c                  57.7334  5.2393 20000  47.5480  68.0473
## bmi_0c~~bmi_0c                  23.6379  2.1547 20000  19.3260  27.7905
## painvas_0c~~painvas_0c           3.9706  0.3647 20000   3.2586   4.6942
## poshee_0c~~poshee_0c             0.7570  0.0691 20000   0.6214   0.8924
## swesourceNA_0c~~swesourceNA_0c   1.3492  0.1254 20000   1.1011   1.5930
## fam3_0~~fam3_0                   0.1470  0.0135 20000   0.1207   0.1734
## coplan_18c~1                    -0.3101  0.2264 20000  -0.7533   0.1324
## mvpaagt_24~1                    36.3783  3.3026 20000  29.8814  42.7697
## groupfu~1                        0.5104  0.0324 20000   0.4462   0.5730
## coplan_0c~1                      0.0000  0.1019 20000  -0.2007   0.1989
## mvpaagt_0c~1                    -0.3148  1.8751 20000  -4.0216   3.2891
## HIE~1                            0.3361  0.0304 20000   0.2763   0.3961
## LIE~1                            0.3402  0.0302 20000   0.2804   0.3998
## sex_0~1                          0.3734  0.0310 20000   0.3131   0.4351
## age_0c~1                         0.0000  0.4898 20000  -0.9710   0.9679
## bmi_0c~1                         0.0000  0.3151 20000  -0.6160   0.6198
## painvas_0c~1                    -0.0032  0.1293 20000  -0.2596   0.2513
## poshee_0c~1                      0.0007  0.0557 20000  -0.1067   0.1103
## swesourceNA_0c~1                 0.0004  0.0763 20000  -0.1491   0.1481
## fam3_0~1                         0.1789  0.0247 20000   0.1299   0.2270
## ind                              0.3143  0.5636 20000  -0.5956   1.6988
## total                            2.8409  2.9623 20000  -2.9661   8.7498

Maintenance Self-Efficacy at 12 Months

model <- '
# Direct Effects
aufswe_12c ~ a1*groupfu + a2*aufswe_0c + a3*mvpaagt_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
mvpaagt_24 ~ c1*groupfu + c2*aufswe_0c + c3*mvpaagt_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*aufswe_12c

# Covariances
groupfu ~~ aufswe_0c + mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
aufswe_0c ~~ mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
mvpaagt_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 407 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        15
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   aufswe_12c ~                                                          
##     groupfu   (a1)   -0.182    0.158   -1.153    0.249   -0.492    0.127
##     aufsw_0c  (a2)    0.114    0.069    1.653    0.098   -0.021    0.249
##     mvpgt_0c  (a3)   -0.000    0.003   -0.116    0.907   -0.006    0.005
##     HIE       (a4)   -0.332    0.189   -1.762    0.078   -0.702    0.037
##     LIE       (a5)   -0.040    0.192   -0.207    0.836   -0.416    0.336
##     sex_0     (a6)   -0.086    0.160   -0.539    0.590   -0.401    0.228
##     age_0c    (a7)   -0.001    0.011   -0.119    0.905   -0.023    0.020
##     bmi_0c    (a8)    0.011    0.017    0.669    0.503   -0.021    0.044
##     panvs_0c  (a9)   -0.081    0.042   -1.946    0.052   -0.162    0.001
##     poshe_0c (a10)    0.121    0.093    1.297    0.195   -0.062    0.303
##     swsrNA_0 (a11)   -0.141    0.080   -1.777    0.076   -0.297    0.015
##     fam3_0   (a12)   -0.072    0.202   -0.356    0.722   -0.468    0.324
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    3.251    3.007    1.081    0.280   -2.643    9.144
##     aufsw_0c  (c2)    0.852    1.323    0.645    0.519   -1.740    3.445
##     mvpgt_0c  (c3)    0.729    0.057   12.823    0.000    0.617    0.840
##     HIE       (c4)   -2.963    3.601   -0.823    0.411  -10.020    4.095
##     LIE       (c5)    3.504    3.651    0.960    0.337   -3.652   10.661
##     sex_0     (c6)   -1.406    2.973   -0.473    0.636   -7.234    4.422
##     age_0c    (c7)   -0.239    0.220   -1.083    0.279   -0.671    0.193
##     bmi_0c    (c8)   -0.227    0.341   -0.666    0.505   -0.896    0.441
##     panvs_0c  (c9)   -1.776    0.838   -2.120    0.034   -3.418   -0.134
##     poshe_0c (c10)    0.369    1.702    0.217    0.828   -2.967    3.705
##     swsrNA_0 (c11)   -2.745    1.464   -1.875    0.061   -5.614    0.125
##     fam3_0   (c12)    4.434    3.861    1.148    0.251   -3.133   12.001
##     afsw_12c  (b1)    0.093    1.461    0.064    0.949   -2.772    2.957
## 
## Covariances:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                             
##     aufswe_0c         -0.066    0.038   -1.767    0.077   -0.140    0.007
##     mvpaagt_0c        -0.753    0.935   -0.805    0.421   -2.586    1.081
##     HIE                0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE               -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0              0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c            -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c            -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c        -0.152    0.065   -2.316    0.021   -0.280   -0.023
##     poshee_0c          0.004    0.028    0.146    0.884   -0.051    0.059
##     swesourceNA_0c     0.069    0.038    1.788    0.074   -0.007    0.144
##     fam3_0            -0.033    0.013   -2.609    0.009   -0.057   -0.008
##   aufswe_0c ~~                                                           
##     mvpaagt_0c         1.264    2.162    0.584    0.559   -2.975    5.502
##     HIE                0.055    0.035    1.540    0.124   -0.015    0.124
##     LIE                0.007    0.035    0.206    0.836   -0.062    0.077
##     sex_0             -0.027    0.036   -0.746    0.455   -0.098    0.044
##     age_0c            -0.053    0.568   -0.093    0.926   -1.166    1.061
##     bmi_0c            -0.409    0.364   -1.122    0.262   -1.123    0.305
##     painvas_0c        -0.102    0.150   -0.684    0.494   -0.396    0.191
##     poshee_0c          0.219    0.067    3.275    0.001    0.088    0.350
##     swesourceNA_0c    -0.066    0.088   -0.750    0.453   -0.240    0.107
##     fam3_0             0.025    0.029    0.851    0.395   -0.032    0.081
##   mvpaagt_0c ~~                                                          
##     HIE               -0.841    0.887   -0.947    0.343   -2.579    0.898
##     LIE                0.141    0.883    0.160    0.873   -1.589    1.872
##     sex_0              0.803    0.908    0.884    0.377   -0.977    2.584
##     age_0c           -51.557   14.498   -3.556    0.000  -79.973  -23.141
##     bmi_0c           -23.435    9.257   -2.532    0.011  -41.578   -5.293
##     painvas_0c         6.583    3.797    1.734    0.083   -0.859   14.026
##     poshee_0c          2.674    1.641    1.629    0.103   -0.543    5.891
##     swesourceNA_0c     1.550    2.207    0.702    0.483   -2.777    5.876
##     fam3_0             0.280    0.708    0.396    0.692   -1.108    1.668
##   HIE ~~                                                                 
##     LIE               -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0             -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c             0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c             0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c        -0.025    0.061   -0.407    0.684   -0.145    0.095
##     poshee_0c          0.045    0.027    1.695    0.090   -0.007    0.098
##     swesourceNA_0c    -0.038    0.036   -1.033    0.302   -0.109    0.034
##     fam3_0             0.002    0.012    0.181    0.856   -0.021    0.025
##   LIE ~~                                                                 
##     sex_0              0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c            -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c            -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c         0.039    0.062    0.632    0.528   -0.082    0.160
##     poshee_0c         -0.040    0.027   -1.511    0.131   -0.093    0.012
##     swesourceNA_0c     0.011    0.036    0.313    0.755   -0.060    0.082
##     fam3_0            -0.006    0.012   -0.550    0.582   -0.030    0.017
##   sex_0 ~~                                                               
##     age_0c             0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c            -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c        -0.008    0.063   -0.129    0.897   -0.131    0.115
##     poshee_0c         -0.034    0.027   -1.262    0.207   -0.088    0.019
##     swesourceNA_0c    -0.073    0.037   -1.972    0.049   -0.146   -0.000
##     fam3_0            -0.025    0.012   -2.096    0.036   -0.049   -0.002
##   age_0c ~~                                                              
##     bmi_0c            -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c        -2.740    0.995   -2.752    0.006   -4.691   -0.789
##     poshee_0c         -1.298    0.434   -2.988    0.003   -2.149   -0.446
##     swesourceNA_0c    -1.115    0.581   -1.919    0.055   -2.254    0.024
##     fam3_0            -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                              
##     painvas_0c         1.812    0.642    2.822    0.005    0.554    3.070
##     poshee_0c          0.084    0.274    0.306    0.760   -0.454    0.621
##     swesourceNA_0c     0.615    0.374    1.641    0.101   -0.119    1.349
##     fam3_0             0.055    0.121    0.457    0.648   -0.181    0.291
##   painvas_0c ~~                                                          
##     poshee_0c         -0.028    0.120   -0.231    0.818   -0.263    0.208
##     swesourceNA_0c     0.350    0.153    2.282    0.022    0.049    0.650
##     fam3_0             0.067    0.051    1.319    0.187   -0.033    0.166
##   poshee_0c ~~                                                           
##     swesourceNA_0c    -0.124    0.067   -1.863    0.062   -0.255    0.006
##     fam3_0             0.002    0.022    0.114    0.909   -0.040    0.045
##   swesourceNA_0c ~~                                                      
##     fam3_0             0.006    0.029    0.189    0.850   -0.052    0.063
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_12c        0.248    0.165    1.502    0.133   -0.075    0.571
##    .mvpaagt_24       35.933    3.305   10.871    0.000   29.455   42.412
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     aufswe_0c         0.000    0.075    0.000    1.000   -0.147    0.147
##     mvpaagt_0c       -0.288    1.869   -0.154    0.878   -3.951    3.376
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c            0.000    0.489    0.000    1.000   -0.959    0.959
##     bmi_0c            0.000    0.313    0.000    1.000   -0.614    0.614
##     painvas_0c       -0.005    0.130   -0.040    0.968   -0.259    0.249
##     poshee_0c         0.002    0.056    0.043    0.966   -0.108    0.113
##     swesourceNA_0c    0.000    0.076    0.006    0.996   -0.149    0.150
##     fam3_0            0.179    0.025    7.227    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_12c        0.929    0.103    8.988    0.000    0.727    1.132
##    .mvpaagt_24      261.598   32.619    8.020    0.000  197.666  325.530
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     aufswe_0c         1.347    0.123   10.977    0.000    1.107    1.588
##     mvpaagt_0c      815.758   75.853   10.754    0.000  667.089  964.427
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.966    0.365   10.864    0.000    3.250    4.681
##     poshee_0c         0.758    0.069   10.941    0.000    0.622    0.894
##     swesourceNA_0c    1.350    0.126   10.743    0.000    1.104    1.596
##     fam3_0            0.147    0.013   10.955    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.017    0.267   -0.063    0.949   -0.540    0.506
##     total             3.234    2.992    1.081    0.280   -2.630    9.098

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                     est      se     R     2.5%    97.5%
## a1                              -0.1822  0.1573 20000  -0.4878   0.1266
## a2                               0.1141  0.0691 20000  -0.0224   0.2507
## a3                              -0.0003  0.0029 20000  -0.0060   0.0054
## a4                              -0.3323  0.1896 20000  -0.7016   0.0399
## a5                              -0.0397  0.1928 20000  -0.4179   0.3388
## a6                              -0.0864  0.1615 20000  -0.4033   0.2285
## a7                              -0.0013  0.0110 20000  -0.0227   0.0203
## a8                               0.0111  0.0166 20000  -0.0213   0.0434
## a9                              -0.0809  0.0419 20000  -0.1629   0.0013
## a10                              0.1209  0.0929 20000  -0.0631   0.3005
## a11                             -0.1413  0.0800 20000  -0.2984   0.0160
## a12                             -0.0720  0.2019 20000  -0.4643   0.3207
## c1                               3.2506  3.0234 20000  -2.6951   9.2082
## c2                               0.8525  1.3261 20000  -1.8041   3.4245
## c3                               0.7286  0.0568 20000   0.6177   0.8401
## c4                              -2.9625  3.6042 20000 -10.0188   4.1546
## c5                               3.5042  3.6327 20000  -3.5268  10.6858
## c6                              -1.4061  2.9406 20000  -7.0824   4.3739
## c7                              -0.2388  0.2211 20000  -0.6698   0.1962
## c8                              -0.2272  0.3420 20000  -0.8882   0.4518
## c9                              -1.7762  0.8375 20000  -3.4144  -0.1606
## c10                              0.3688  1.6870 20000  -2.9649   3.6803
## c11                             -2.7448  1.4595 20000  -5.6353   0.1043
## c12                              4.4341  3.8301 20000  -3.0333  11.9758
## b1                               0.0928  1.4817 20000  -2.8455   2.9605
## groupfu~~aufswe_0c              -0.0665  0.0376 20000  -0.1396   0.0068
## groupfu~~mvpaagt_0c             -0.7526  0.9372 20000  -2.5943   1.0698
## groupfu~~HIE                     0.0069  0.0153 20000  -0.0233   0.0367
## groupfu~~LIE                    -0.0035  0.0152 20000  -0.0334   0.0261
## groupfu~~sex_0                   0.0003  0.0156 20000  -0.0297   0.0308
## groupfu~~age_0c                 -0.0706  0.2457 20000  -0.5596   0.4049
## groupfu~~bmi_0c                 -0.0398  0.1562 20000  -0.3466   0.2683
## groupfu~~painvas_0c             -0.1516  0.0662 20000  -0.2824  -0.0210
## groupfu~~poshee_0c               0.0041  0.0280 20000  -0.0504   0.0591
## groupfu~~swesourceNA_0c          0.0688  0.0386 20000  -0.0060   0.1455
## groupfu~~fam3_0                 -0.0327  0.0126 20000  -0.0571  -0.0078
## aufswe_0c~~mvpaagt_0c            1.2638  2.1563 20000  -2.9021   5.5287
## aufswe_0c~~HIE                   0.0547  0.0357 20000  -0.0146   0.1233
## aufswe_0c~~LIE                   0.0073  0.0356 20000  -0.0620   0.0782
## aufswe_0c~~sex_0                -0.0270  0.0362 20000  -0.0978   0.0447
## aufswe_0c~~age_0c               -0.0527  0.5680 20000  -1.1633   1.0576
## aufswe_0c~~bmi_0c               -0.4089  0.3635 20000  -1.1107   0.3057
## aufswe_0c~~painvas_0c           -0.1025  0.1503 20000  -0.3955   0.1918
## aufswe_0c~~poshee_0c             0.2190  0.0661 20000   0.0906   0.3492
## aufswe_0c~~swesourceNA_0c       -0.0664  0.0886 20000  -0.2393   0.1085
## aufswe_0c~~fam3_0                0.0245  0.0288 20000  -0.0325   0.0803
## mvpaagt_0c~~HIE                 -0.8406  0.8842 20000  -2.5816   0.8972
## mvpaagt_0c~~LIE                  0.1413  0.8857 20000  -1.6013   1.8723
## mvpaagt_0c~~sex_0                0.8031  0.9078 20000  -0.9623   2.5787
## mvpaagt_0c~~age_0c             -51.5571 14.5053 20000 -79.9457 -22.9027
## mvpaagt_0c~~bmi_0c             -23.4354  9.2538 20000 -41.6387  -5.2714
## mvpaagt_0c~~painvas_0c           6.5833  3.7889 20000  -0.7184  14.1596
## mvpaagt_0c~~poshee_0c            2.6744  1.6469 20000  -0.5549   5.9061
## mvpaagt_0c~~swesourceNA_0c       1.5496  2.2351 20000  -2.8296   5.9194
## mvpaagt_0c~~fam3_0               0.2803  0.7098 20000  -1.1035   1.6713
## HIE~~LIE                        -0.1144  0.0162 20000  -0.1470  -0.0826
## HIE~~sex_0                      -0.0010  0.0147 20000  -0.0302   0.0274
## HIE~~age_0c                      0.1878  0.2340 20000  -0.2772   0.6499
## HIE~~bmi_0c                      0.3432  0.1506 20000   0.0493   0.6399
## HIE~~painvas_0c                 -0.0248  0.0608 20000  -0.1449   0.0942
## HIE~~poshee_0c                   0.0454  0.0267 20000  -0.0075   0.0979
## HIE~~swesourceNA_0c             -0.0375  0.0361 20000  -0.1076   0.0332
## HIE~~fam3_0                      0.0021  0.0117 20000  -0.0208   0.0249
## LIE~~sex_0                       0.0016  0.0148 20000  -0.0269   0.0309
## LIE~~age_0c                     -0.2255  0.2324 20000  -0.6732   0.2311
## LIE~~bmi_0c                     -0.3705  0.1514 20000  -0.6666  -0.0741
## LIE~~painvas_0c                  0.0389  0.0613 20000  -0.0816   0.1593
## LIE~~poshee_0c                  -0.0404  0.0268 20000  -0.0921   0.0129
## LIE~~swesourceNA_0c              0.0113  0.0362 20000  -0.0602   0.0824
## LIE~~fam3_0                     -0.0065  0.0117 20000  -0.0292   0.0167
## sex_0~~age_0c                    0.0533  0.2368 20000  -0.4119   0.5194
## sex_0~~bmi_0c                   -0.1011  0.1525 20000  -0.4045   0.1958
## sex_0~~painvas_0c               -0.0081  0.0630 20000  -0.1318   0.1142
## sex_0~~poshee_0c                -0.0344  0.0273 20000  -0.0883   0.0191
## sex_0~~swesourceNA_0c           -0.0733  0.0371 20000  -0.1467  -0.0006
## sex_0~~fam3_0                   -0.0253  0.0121 20000  -0.0490  -0.0015
## age_0c~~bmi_0c                  -1.6174  2.4052 20000  -6.3596   3.0836
## age_0c~~painvas_0c              -2.7400  0.9888 20000  -4.6548  -0.7924
## age_0c~~poshee_0c               -1.2978  0.4337 20000  -2.1484  -0.4435
## age_0c~~swesourceNA_0c          -1.1152  0.5867 20000  -2.2616   0.0343
## age_0c~~fam3_0                  -0.0283  0.1859 20000  -0.3908   0.3353
## bmi_0c~~painvas_0c               1.8116  0.6423 20000   0.5437   3.0665
## bmi_0c~~poshee_0c                0.0838  0.2718 20000  -0.4471   0.6116
## bmi_0c~~swesourceNA_0c           0.6147  0.3744 20000  -0.1293   1.3415
## bmi_0c~~fam3_0                   0.0551  0.1205 20000  -0.1838   0.2899
## painvas_0c~~poshee_0c           -0.0277  0.1193 20000  -0.2619   0.2072
## painvas_0c~~swesourceNA_0c       0.3499  0.1550 20000   0.0486   0.6516
## painvas_0c~~fam3_0               0.0670  0.0505 20000  -0.0318   0.1656
## poshee_0c~~swesourceNA_0c       -0.1243  0.0668 20000  -0.2542   0.0055
## poshee_0c~~fam3_0                0.0025  0.0214 20000  -0.0392   0.0444
## swesourceNA_0c~~fam3_0           0.0055  0.0292 20000  -0.0520   0.0634
## aufswe_12c~~aufswe_12c           0.9293  0.1033 20000   0.7246   1.1320
## mvpaagt_24~~mvpaagt_24         261.5983 32.6800 20000 198.3456 326.1270
## groupfu~~groupfu                 0.2499  0.0227 20000   0.2055   0.2946
## aufswe_0c~~aufswe_0c             1.3474  0.1228 20000   1.1037   1.5877
## mvpaagt_0c~~mvpaagt_0c         815.7581 75.6078 20000 666.5583 962.4298
## HIE~~HIE                         0.2231  0.0203 20000   0.1838   0.2628
## LIE~~LIE                         0.2245  0.0207 20000   0.1839   0.2654
## sex_0~~sex_0                     0.2340  0.0212 20000   0.1929   0.2762
## age_0c~~age_0c                  57.7334  5.2409 20000  47.4235  68.0392
## bmi_0c~~bmi_0c                  23.6379  2.1557 20000  19.3938  27.8134
## painvas_0c~~painvas_0c           3.9655  0.3656 20000   3.2561   4.6813
## poshee_0c~~poshee_0c             0.7583  0.0694 20000   0.6222   0.8938
## swesourceNA_0c~~swesourceNA_0c   1.3500  0.1256 20000   1.1033   1.5971
## fam3_0~~fam3_0                   0.1471  0.0135 20000   0.1205   0.1732
## aufswe_12c~1                     0.2476  0.1663 20000  -0.0770   0.5754
## mvpaagt_24~1                    35.9331  3.3045 20000  29.3519  42.4238
## groupfu~1                        0.5104  0.0324 20000   0.4476   0.5739
## aufswe_0c~1                      0.0000  0.0742 20000  -0.1455   0.1456
## mvpaagt_0c~1                    -0.2878  1.8781 20000  -3.9255   3.4000
## HIE~1                            0.3361  0.0302 20000   0.2763   0.3955
## LIE~1                            0.3402  0.0305 20000   0.2801   0.4002
## sex_0~1                          0.3734  0.0311 20000   0.3121   0.4347
## age_0c~1                         0.0000  0.4904 20000  -0.9664   0.9770
## bmi_0c~1                         0.0000  0.3119 20000  -0.6193   0.6058
## painvas_0c~1                    -0.0052  0.1293 20000  -0.2575   0.2489
## poshee_0c~1                      0.0024  0.0558 20000  -0.1073   0.1116
## swesourceNA_0c~1                 0.0004  0.0760 20000  -0.1483   0.1494
## fam3_0~1                         0.1789  0.0247 20000   0.1305   0.2274
## ind                             -0.0169  0.3599 20000  -0.8138   0.7585
## total                            3.2337  3.0134 20000  -2.6757   9.1557

Maintenance Self-Efficacy at 18 Months

model <- '
# Direct Effects
aufswe_18c ~ a1*groupfu + a2*aufswe_0c + a3*mvpaagt_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
mvpaagt_24 ~ c1*groupfu + c2*aufswe_0c + c3*mvpaagt_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*aufswe_18c

# Covariances
groupfu ~~ aufswe_0c + mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
aufswe_0c ~~ mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
mvpaagt_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 409 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        15
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   aufswe_18c ~                                                          
##     groupfu   (a1)   -0.022    0.170   -0.132    0.895   -0.357    0.312
##     aufsw_0c  (a2)    0.214    0.073    2.909    0.004    0.070    0.358
##     mvpgt_0c  (a3)    0.003    0.003    0.926    0.355   -0.003    0.009
##     HIE       (a4)   -0.106    0.205   -0.516    0.606   -0.508    0.296
##     LIE       (a5)    0.002    0.209    0.009    0.993   -0.409    0.412
##     sex_0     (a6)   -0.132    0.175   -0.754    0.451   -0.475    0.211
##     age_0c    (a7)    0.018    0.012    1.424    0.154   -0.007    0.042
##     bmi_0c    (a8)   -0.017    0.018   -0.934    0.350   -0.053    0.019
##     panvs_0c  (a9)    0.005    0.048    0.097    0.923   -0.089    0.098
##     poshe_0c (a10)    0.208    0.098    2.123    0.034    0.016    0.400
##     swsrNA_0 (a11)   -0.104    0.087   -1.199    0.231   -0.275    0.066
##     fam3_0   (a12)   -0.097    0.218   -0.446    0.655   -0.525    0.330
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    2.993    2.993    1.000    0.317   -2.873    8.859
##     aufsw_0c  (c2)    0.656    1.330    0.493    0.622   -1.952    3.264
##     mvpgt_0c  (c3)    0.720    0.057   12.558    0.000    0.608    0.832
##     HIE       (c4)   -2.893    3.538   -0.818    0.413   -9.827    4.040
##     LIE       (c5)    3.629    3.637    0.998    0.319   -3.501   10.758
##     sex_0     (c6)   -1.199    2.970   -0.404    0.686   -7.020    4.621
##     age_0c    (c7)   -0.270    0.222   -1.219    0.223   -0.705    0.164
##     bmi_0c    (c8)   -0.192    0.341   -0.564    0.573   -0.861    0.476
##     panvs_0c  (c9)   -1.787    0.831   -2.150    0.032   -3.417   -0.158
##     poshe_0c (c10)    0.095    1.714    0.055    0.956   -3.264    3.453
##     swsrNA_0 (c11)   -2.559    1.459   -1.754    0.080   -5.419    0.301
##     fam3_0   (c12)    4.417    3.843    1.149    0.250   -3.116   11.950
##     afsw_18c  (b1)    1.468    1.478    0.993    0.321   -1.429    4.366
## 
## Covariances:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                             
##     aufswe_0c         -0.066    0.038   -1.767    0.077   -0.140    0.007
##     mvpaagt_0c        -0.750    0.935   -0.802    0.423   -2.583    1.083
##     HIE                0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE               -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0              0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c            -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c            -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c        -0.151    0.065   -2.300    0.021   -0.279   -0.022
##     poshee_0c          0.004    0.028    0.149    0.881   -0.051    0.059
##     swesourceNA_0c     0.070    0.038    1.812    0.070   -0.006    0.145
##     fam3_0            -0.033    0.013   -2.609    0.009   -0.057   -0.008
##   aufswe_0c ~~                                                           
##     mvpaagt_0c         1.264    2.162    0.585    0.559   -2.974    5.502
##     HIE                0.055    0.035    1.540    0.124   -0.015    0.124
##     LIE                0.007    0.035    0.206    0.836   -0.062    0.077
##     sex_0             -0.027    0.036   -0.746    0.455   -0.098    0.044
##     age_0c            -0.053    0.568   -0.093    0.926   -1.166    1.061
##     bmi_0c            -0.409    0.364   -1.122    0.262   -1.123    0.305
##     painvas_0c        -0.104    0.150   -0.695    0.487   -0.398    0.189
##     poshee_0c          0.219    0.067    3.280    0.001    0.088    0.350
##     swesourceNA_0c    -0.065    0.088   -0.739    0.460   -0.239    0.108
##     fam3_0             0.025    0.029    0.851    0.395   -0.032    0.081
##   mvpaagt_0c ~~                                                          
##     HIE               -0.839    0.887   -0.946    0.344   -2.578    0.899
##     LIE                0.140    0.883    0.159    0.874   -1.590    1.871
##     sex_0              0.803    0.908    0.884    0.377   -0.978    2.583
##     age_0c           -51.548   14.497   -3.556    0.000  -79.962  -23.133
##     bmi_0c           -23.424    9.256   -2.531    0.011  -41.565   -5.283
##     painvas_0c         6.603    3.798    1.738    0.082   -0.841   14.048
##     poshee_0c          2.673    1.641    1.629    0.103   -0.544    5.890
##     swesourceNA_0c     1.532    2.208    0.694    0.488   -2.795    5.860
##     fam3_0             0.281    0.708    0.396    0.692   -1.108    1.669
##   HIE ~~                                                                 
##     LIE               -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0             -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c             0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c             0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c        -0.026    0.061   -0.422    0.673   -0.145    0.094
##     poshee_0c          0.045    0.027    1.700    0.089   -0.007    0.098
##     swesourceNA_0c    -0.036    0.036   -0.997    0.319   -0.107    0.035
##     fam3_0             0.002    0.012    0.181    0.856   -0.021    0.025
##   LIE ~~                                                                 
##     sex_0              0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c            -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c            -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c         0.040    0.062    0.642    0.521   -0.081    0.160
##     poshee_0c         -0.040    0.027   -1.514    0.130   -0.093    0.012
##     swesourceNA_0c     0.010    0.036    0.288    0.774   -0.060    0.081
##     fam3_0            -0.006    0.012   -0.550    0.582   -0.030    0.017
##   sex_0 ~~                                                               
##     age_0c             0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c            -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c        -0.008    0.063   -0.133    0.894   -0.131    0.114
##     poshee_0c         -0.034    0.027   -1.265    0.206   -0.088    0.019
##     swesourceNA_0c    -0.074    0.037   -1.988    0.047   -0.147   -0.001
##     fam3_0            -0.025    0.012   -2.096    0.036   -0.049   -0.002
##   age_0c ~~                                                              
##     bmi_0c            -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c        -2.740    0.995   -2.753    0.006   -4.691   -0.789
##     poshee_0c         -1.297    0.434   -2.986    0.003   -2.149   -0.446
##     swesourceNA_0c    -1.112    0.581   -1.913    0.056   -2.251    0.027
##     fam3_0            -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                              
##     painvas_0c         1.799    0.642    2.803    0.005    0.541    3.057
##     poshee_0c          0.086    0.274    0.312    0.755   -0.452    0.623
##     swesourceNA_0c     0.616    0.375    1.643    0.100   -0.119    1.350
##     fam3_0             0.055    0.121    0.457    0.648   -0.181    0.291
##   painvas_0c ~~                                                          
##     poshee_0c         -0.028    0.121   -0.231    0.817   -0.264    0.208
##     swesourceNA_0c     0.346    0.153    2.259    0.024    0.046    0.647
##     fam3_0             0.066    0.051    1.308    0.191   -0.033    0.166
##   poshee_0c ~~                                                           
##     swesourceNA_0c    -0.124    0.067   -1.851    0.064   -0.254    0.007
##     fam3_0             0.002    0.022    0.112    0.911   -0.040    0.045
##   swesourceNA_0c ~~                                                      
##     fam3_0             0.006    0.029    0.196    0.844   -0.052    0.063
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_18c        0.118    0.181    0.649    0.516   -0.238    0.473
##    .mvpaagt_24       36.016    3.267   11.025    0.000   29.613   42.418
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     aufswe_0c        -0.000    0.075   -0.000    1.000   -0.147    0.147
##     mvpaagt_0c       -0.289    1.869   -0.155    0.877   -3.953    3.374
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c            0.000    0.489    0.000    1.000   -0.959    0.959
##     bmi_0c            0.000    0.313    0.000    1.000   -0.614    0.614
##     painvas_0c       -0.004    0.130   -0.033    0.974   -0.258    0.250
##     poshee_0c         0.003    0.056    0.047    0.963   -0.108    0.113
##     swesourceNA_0c    0.002    0.076    0.027    0.979   -0.148    0.152
##     fam3_0            0.179    0.025    7.227    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_18c        1.030    0.118    8.710    0.000    0.799    1.262
##    .mvpaagt_24      259.580   32.357    8.022    0.000  196.162  322.998
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     aufswe_0c         1.347    0.123   10.977    0.000    1.107    1.588
##     mvpaagt_0c      815.657   75.842   10.755    0.000  667.010  964.304
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.964    0.365   10.867    0.000    3.249    4.679
##     poshee_0c         0.758    0.069   10.939    0.000    0.623    0.894
##     swesourceNA_0c    1.350    0.126   10.740    0.000    1.104    1.597
##     fam3_0            0.147    0.013   10.955    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.033    0.251   -0.132    0.895   -0.525    0.459
##     total             2.960    3.005    0.985    0.325   -2.929    8.849

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                     est      se     R     2.5%    97.5%
## a1                              -0.0225  0.1720 20000  -0.3604   0.3121
## a2                               0.2138  0.0740 20000   0.0680   0.3599
## a3                               0.0029  0.0031 20000  -0.0032   0.0090
## a4                              -0.1059  0.2069 20000  -0.5112   0.3009
## a5                               0.0018  0.2097 20000  -0.4067   0.4162
## a6                              -0.1318  0.1735 20000  -0.4700   0.2086
## a7                               0.0178  0.0125 20000  -0.0068   0.0423
## a8                              -0.0171  0.0185 20000  -0.0533   0.0192
## a9                               0.0046  0.0479 20000  -0.0879   0.1001
## a10                              0.2079  0.0987 20000   0.0138   0.4017
## a11                             -0.1044  0.0868 20000  -0.2751   0.0648
## a12                             -0.0974  0.2203 20000  -0.5288   0.3352
## c1                               2.9932  2.9871 20000  -2.8336   8.9539
## c2                               0.6559  1.3299 20000  -1.9543   3.2643
## c3                               0.7199  0.0575 20000   0.6080   0.8329
## c4                              -2.8934  3.5353 20000  -9.8508   4.0307
## c5                               3.6286  3.6438 20000  -3.4806  10.8655
## c6                              -1.1994  2.9578 20000  -6.9478   4.6267
## c7                              -0.2703  0.2221 20000  -0.7108   0.1626
## c8                              -0.1924  0.3412 20000  -0.8534   0.4806
## c9                              -1.7874  0.8407 20000  -3.4352  -0.1412
## c10                              0.0949  1.7077 20000  -3.2260   3.4574
## c11                             -2.5591  1.4733 20000  -5.4681   0.3029
## c12                              4.4173  3.8096 20000  -3.0210  11.8507
## b1                               1.4681  1.4813 20000  -1.4749   4.3368
## groupfu~~aufswe_0c              -0.0665  0.0374 20000  -0.1397   0.0060
## groupfu~~mvpaagt_0c             -0.7500  0.9377 20000  -2.5674   1.0867
## groupfu~~HIE                     0.0069  0.0152 20000  -0.0233   0.0366
## groupfu~~LIE                    -0.0035  0.0153 20000  -0.0330   0.0266
## groupfu~~sex_0                   0.0003  0.0156 20000  -0.0302   0.0305
## groupfu~~age_0c                 -0.0706  0.2447 20000  -0.5547   0.4084
## groupfu~~bmi_0c                 -0.0398  0.1556 20000  -0.3457   0.2669
## groupfu~~painvas_0c             -0.1505  0.0659 20000  -0.2806  -0.0224
## groupfu~~poshee_0c               0.0042  0.0280 20000  -0.0507   0.0592
## groupfu~~swesourceNA_0c          0.0698  0.0384 20000  -0.0064   0.1453
## groupfu~~fam3_0                 -0.0327  0.0127 20000  -0.0574  -0.0078
## aufswe_0c~~mvpaagt_0c            1.2641  2.1860 20000  -3.0580   5.5473
## aufswe_0c~~HIE                   0.0547  0.0356 20000  -0.0153   0.1242
## aufswe_0c~~LIE                   0.0073  0.0353 20000  -0.0620   0.0764
## aufswe_0c~~sex_0                -0.0270  0.0362 20000  -0.0974   0.0436
## aufswe_0c~~age_0c               -0.0527  0.5733 20000  -1.1672   1.0719
## aufswe_0c~~bmi_0c               -0.4089  0.3638 20000  -1.1097   0.3058
## aufswe_0c~~painvas_0c           -0.1041  0.1497 20000  -0.3940   0.1900
## aufswe_0c~~poshee_0c             0.2193  0.0671 20000   0.0887   0.3507
## aufswe_0c~~swesourceNA_0c       -0.0654  0.0883 20000  -0.2365   0.1094
## aufswe_0c~~fam3_0                0.0245  0.0287 20000  -0.0309   0.0804
## mvpaagt_0c~~HIE                 -0.8392  0.8903 20000  -2.5793   0.8915
## mvpaagt_0c~~LIE                  0.1401  0.8784 20000  -1.5754   1.8462
## mvpaagt_0c~~sex_0                0.8028  0.9101 20000  -0.9805   2.5819
## mvpaagt_0c~~age_0c             -51.5475 14.4602 20000 -79.8112 -22.8097
## mvpaagt_0c~~bmi_0c             -23.4240  9.2836 20000 -41.7992  -5.4194
## mvpaagt_0c~~painvas_0c           6.6034  3.7879 20000  -0.9239  13.8835
## mvpaagt_0c~~poshee_0c            2.6733  1.6463 20000  -0.5465   5.8868
## mvpaagt_0c~~swesourceNA_0c       1.5320  2.2014 20000  -2.8157   5.9038
## mvpaagt_0c~~fam3_0               0.2806  0.7097 20000  -1.1202   1.6711
## HIE~~LIE                        -0.1144  0.0161 20000  -0.1458  -0.0822
## HIE~~sex_0                      -0.0010  0.0147 20000  -0.0298   0.0280
## HIE~~age_0c                      0.1878  0.2331 20000  -0.2713   0.6424
## HIE~~bmi_0c                      0.3432  0.1487 20000   0.0519   0.6353
## HIE~~painvas_0c                 -0.0258  0.0613 20000  -0.1470   0.0931
## HIE~~poshee_0c                   0.0455  0.0267 20000  -0.0065   0.0980
## HIE~~swesourceNA_0c             -0.0362  0.0364 20000  -0.1071   0.0351
## HIE~~fam3_0                      0.0021  0.0116 20000  -0.0206   0.0248
## LIE~~sex_0                       0.0016  0.0148 20000  -0.0271   0.0308
## LIE~~age_0c                     -0.2255  0.2340 20000  -0.6837   0.2272
## LIE~~bmi_0c                     -0.3705  0.1502 20000  -0.6652  -0.0796
## LIE~~painvas_0c                  0.0396  0.0617 20000  -0.0825   0.1595
## LIE~~poshee_0c                  -0.0405  0.0269 20000  -0.0934   0.0122
## LIE~~swesourceNA_0c              0.0104  0.0363 20000  -0.0603   0.0819
## LIE~~fam3_0                     -0.0065  0.0117 20000  -0.0296   0.0165
## sex_0~~age_0c                    0.0533  0.2358 20000  -0.4116   0.5140
## sex_0~~bmi_0c                   -0.1011  0.1528 20000  -0.3971   0.1994
## sex_0~~painvas_0c               -0.0083  0.0625 20000  -0.1298   0.1150
## sex_0~~poshee_0c                -0.0345  0.0274 20000  -0.0884   0.0188
## sex_0~~swesourceNA_0c           -0.0740  0.0374 20000  -0.1462  -0.0006
## sex_0~~fam3_0                   -0.0253  0.0120 20000  -0.0488  -0.0016
## age_0c~~bmi_0c                  -1.6174  2.4110 20000  -6.3850   3.0802
## age_0c~~painvas_0c              -2.7400  0.9937 20000  -4.6814  -0.7983
## age_0c~~poshee_0c               -1.2973  0.4333 20000  -2.1355  -0.4537
## age_0c~~swesourceNA_0c          -1.1117  0.5823 20000  -2.2657   0.0355
## age_0c~~fam3_0                  -0.0283  0.1884 20000  -0.3958   0.3417
## bmi_0c~~painvas_0c               1.7991  0.6398 20000   0.5244   3.0589
## bmi_0c~~poshee_0c                0.0856  0.2734 20000  -0.4511   0.6265
## bmi_0c~~swesourceNA_0c           0.6158  0.3745 20000  -0.1263   1.3409
## bmi_0c~~fam3_0                   0.0551  0.1194 20000  -0.1734   0.2888
## painvas_0c~~poshee_0c           -0.0279  0.1203 20000  -0.2664   0.2067
## painvas_0c~~swesourceNA_0c       0.3464  0.1537 20000   0.0451   0.6455
## painvas_0c~~fam3_0               0.0665  0.0505 20000  -0.0331   0.1656
## poshee_0c~~swesourceNA_0c       -0.1236  0.0672 20000  -0.2555   0.0082
## poshee_0c~~fam3_0                0.0024  0.0214 20000  -0.0400   0.0443
## swesourceNA_0c~~fam3_0           0.0058  0.0293 20000  -0.0519   0.0629
## aufswe_18c~~aufswe_18c           1.0305  0.1194 20000   0.7948   1.2614
## mvpaagt_24~~mvpaagt_24         259.5803 32.4180 20000 196.9052 323.6348
## groupfu~~groupfu                 0.2499  0.0228 20000   0.2057   0.2946
## aufswe_0c~~aufswe_0c             1.3474  0.1224 20000   1.1083   1.5879
## mvpaagt_0c~~mvpaagt_0c         815.6572 75.6011 20000 669.5039 964.8378
## HIE~~HIE                         0.2231  0.0203 20000   0.1831   0.2630
## LIE~~LIE                         0.2245  0.0205 20000   0.1844   0.2649
## sex_0~~sex_0                     0.2340  0.0213 20000   0.1922   0.2754
## age_0c~~age_0c                  57.7334  5.2395 20000  47.5364  68.0591
## bmi_0c~~bmi_0c                  23.6379  2.1546 20000  19.4101  27.7929
## painvas_0c~~painvas_0c           3.9638  0.3629 20000   3.2538   4.6830
## poshee_0c~~poshee_0c             0.7585  0.0697 20000   0.6237   0.8964
## swesourceNA_0c~~swesourceNA_0c   1.3503  0.1259 20000   1.1041   1.6001
## fam3_0~~fam3_0                   0.1471  0.0134 20000   0.1211   0.1735
## aufswe_18c~1                     0.1177  0.1820 20000  -0.2400   0.4730
## mvpaagt_24~1                    36.0158  3.2459 20000  29.5909  42.3535
## groupfu~1                        0.5104  0.0323 20000   0.4473   0.5737
## aufswe_0c~1                      0.0000  0.0742 20000  -0.1456   0.1464
## mvpaagt_0c~1                    -0.2894  1.8769 20000  -3.9952   3.3433
## HIE~1                            0.3361  0.0304 20000   0.2766   0.3956
## LIE~1                            0.3402  0.0302 20000   0.2805   0.4000
## sex_0~1                          0.3734  0.0312 20000   0.3126   0.4346
## age_0c~1                         0.0000  0.4863 20000  -0.9439   0.9563
## bmi_0c~1                         0.0000  0.3128 20000  -0.6088   0.6172
## painvas_0c~1                    -0.0043  0.1299 20000  -0.2587   0.2501
## poshee_0c~1                      0.0026  0.0560 20000  -0.1076   0.1121
## swesourceNA_0c~1                 0.0021  0.0768 20000  -0.1482   0.1518
## fam3_0~1                         0.1789  0.0248 20000   0.1307   0.2280
## ind                             -0.0330  0.3604 20000  -0.8327   0.7483
## total                            2.9602  3.0096 20000  -2.9407   8.9507

Recovery Self-Efficacy at 12 Months

model <- '
# Direct Effects
wieswe_12c ~ a1*groupfu + a2*wieswe_0c + a3*mvpaagt_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
mvpaagt_24 ~ c1*groupfu + c2*wieswe_0c + c3*mvpaagt_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*wieswe_12c

# Covariances
groupfu ~~ wieswe_0c + mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
wieswe_0c ~~ mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
mvpaagt_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 398 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        16
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wieswe_12c ~                                                          
##     groupfu   (a1)    0.264    0.147    1.795    0.073   -0.024    0.552
##     wiesw_0c  (a2)    0.188    0.077    2.437    0.015    0.037    0.339
##     mvpgt_0c  (a3)    0.001    0.003    0.356    0.722   -0.004    0.006
##     HIE       (a4)   -0.113    0.176   -0.642    0.521   -0.457    0.231
##     LIE       (a5)   -0.150    0.178   -0.842    0.400   -0.500    0.199
##     sex_0     (a6)    0.015    0.149    0.102    0.919   -0.277    0.307
##     age_0c    (a7)   -0.009    0.010   -0.827    0.408   -0.029    0.012
##     bmi_0c    (a8)   -0.023    0.016   -1.460    0.144   -0.054    0.008
##     panvs_0c  (a9)    0.018    0.039    0.451    0.652   -0.059    0.094
##     poshe_0c (a10)    0.041    0.088    0.471    0.637   -0.131    0.214
##     swsrNA_0 (a11)   -0.184    0.074   -2.472    0.013   -0.329   -0.038
##     fam3_0   (a12)    0.146    0.189    0.775    0.438   -0.224    0.516
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    2.100    2.992    0.702    0.483   -3.765    7.964
##     wiesw_0c  (c2)   -0.321    1.518   -0.211    0.833   -3.295    2.654
##     mvpgt_0c  (c3)    0.726    0.056   12.849    0.000    0.615    0.836
##     HIE       (c4)   -2.361    3.524   -0.670    0.503   -9.268    4.546
##     LIE       (c5)    4.436    3.638    1.219    0.223   -2.694   11.566
##     sex_0     (c6)   -1.516    2.940   -0.515    0.606   -7.278    4.247
##     age_0c    (c7)   -0.190    0.217   -0.875    0.382   -0.615    0.235
##     bmi_0c    (c8)   -0.207    0.341   -0.606    0.544   -0.875    0.462
##     panvs_0c  (c9)   -1.891    0.823   -2.298    0.022   -3.504   -0.279
##     poshe_0c (c10)    0.500    1.698    0.294    0.769   -2.828    3.827
##     swsrNA_0 (c11)   -2.152    1.530   -1.407    0.160   -5.151    0.846
##     fam3_0   (c12)    4.089    3.825    1.069    0.285   -3.407   11.586
##     wisw_12c  (b1)    2.789    1.581    1.764    0.078   -0.309    5.887
## 
## Covariances:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                             
##     wieswe_0c         -0.063    0.033   -1.932    0.053   -0.127    0.001
##     mvpaagt_0c        -0.754    0.936   -0.806    0.420   -2.588    1.080
##     HIE                0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE               -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0              0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c            -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c            -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c        -0.150    0.065   -2.299    0.021   -0.279   -0.022
##     poshee_0c          0.004    0.028    0.144    0.886   -0.051    0.059
##     swesourceNA_0c     0.067    0.038    1.750    0.080   -0.008    0.142
##     fam3_0            -0.033    0.013   -2.606    0.009   -0.057   -0.008
##   wieswe_0c ~~                                                           
##     mvpaagt_0c         3.785    1.885    2.008    0.045    0.091    7.479
##     HIE                0.020    0.031    0.661    0.509   -0.040    0.080
##     LIE                0.016    0.031    0.528    0.598   -0.044    0.077
##     sex_0             -0.006    0.031   -0.190    0.850   -0.067    0.056
##     age_0c            -0.958    0.495   -1.934    0.053   -1.928    0.013
##     bmi_0c            -0.816    0.319   -2.554    0.011   -1.442   -0.190
##     painvas_0c        -0.053    0.131   -0.407    0.684   -0.309    0.203
##     poshee_0c          0.272    0.059    4.592    0.000    0.156    0.388
##     swesourceNA_0c    -0.219    0.077   -2.835    0.005   -0.370   -0.067
##     fam3_0             0.020    0.025    0.824    0.410   -0.028    0.069
##   mvpaagt_0c ~~                                                          
##     HIE               -0.841    0.887   -0.947    0.344   -2.580    0.899
##     LIE                0.128    0.883    0.145    0.884   -1.603    1.860
##     sex_0              0.820    0.909    0.902    0.367   -0.962    2.601
##     age_0c           -51.687   14.505   -3.563    0.000  -80.116  -23.258
##     bmi_0c           -23.557    9.261   -2.544    0.011  -41.707   -5.406
##     painvas_0c         6.616    3.799    1.742    0.082   -0.830   14.063
##     poshee_0c          2.689    1.642    1.638    0.101   -0.529    5.907
##     swesourceNA_0c     1.564    2.206    0.709    0.478   -2.760    5.887
##     fam3_0             0.284    0.709    0.401    0.689   -1.105    1.673
##   HIE ~~                                                                 
##     LIE               -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0             -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c             0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c             0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c        -0.026    0.061   -0.422    0.673   -0.145    0.094
##     poshee_0c          0.045    0.027    1.693    0.090   -0.007    0.098
##     swesourceNA_0c    -0.035    0.036   -0.976    0.329   -0.106    0.036
##     fam3_0             0.002    0.012    0.179    0.858   -0.021    0.025
##   LIE ~~                                                                 
##     sex_0              0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c            -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c            -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c         0.040    0.062    0.649    0.517   -0.081    0.161
##     poshee_0c         -0.040    0.027   -1.511    0.131   -0.093    0.012
##     swesourceNA_0c     0.009    0.036    0.261    0.794   -0.061    0.080
##     fam3_0            -0.006    0.012   -0.545    0.586   -0.029    0.017
##   sex_0 ~~                                                               
##     age_0c             0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c            -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c        -0.008    0.063   -0.135    0.893   -0.131    0.114
##     poshee_0c         -0.034    0.027   -1.261    0.207   -0.088    0.019
##     swesourceNA_0c    -0.073    0.037   -1.957    0.050   -0.145    0.000
##     fam3_0            -0.025    0.012   -2.099    0.036   -0.049   -0.002
##   age_0c ~~                                                              
##     bmi_0c            -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c        -2.738    0.995   -2.751    0.006   -4.689   -0.787
##     poshee_0c         -1.298    0.434   -2.988    0.003   -2.149   -0.447
##     swesourceNA_0c    -1.108    0.581   -1.908    0.056   -2.246    0.030
##     fam3_0            -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                              
##     painvas_0c         1.799    0.642    2.803    0.005    0.541    3.057
##     poshee_0c          0.083    0.274    0.303    0.762   -0.454    0.621
##     swesourceNA_0c     0.616    0.374    1.647    0.100   -0.117    1.349
##     fam3_0             0.056    0.121    0.461    0.645   -0.181    0.292
##   painvas_0c ~~                                                          
##     poshee_0c         -0.028    0.120   -0.233    0.816   -0.264    0.208
##     swesourceNA_0c     0.349    0.153    2.277    0.023    0.049    0.649
##     fam3_0             0.067    0.051    1.321    0.187   -0.032    0.167
##   poshee_0c ~~                                                           
##     swesourceNA_0c    -0.126    0.067   -1.889    0.059   -0.257    0.005
##     fam3_0             0.002    0.022    0.113    0.910   -0.040    0.045
##   swesourceNA_0c ~~                                                      
##     fam3_0             0.007    0.029    0.223    0.824   -0.051    0.064
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_12c       -0.065    0.154   -0.424    0.671   -0.366    0.236
##    .mvpaagt_24       35.982    3.247   11.080    0.000   29.617   42.346
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     wieswe_0c         0.003    0.065    0.045    0.964   -0.124    0.130
##     mvpaagt_0c       -0.311    1.870   -0.166    0.868   -3.975    3.354
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c           -0.000    0.489   -0.000    1.000   -0.959    0.959
##     bmi_0c            0.000    0.313    0.000    1.000   -0.614    0.614
##     painvas_0c       -0.004    0.130   -0.029    0.977   -0.258    0.250
##     poshee_0c         0.002    0.056    0.041    0.967   -0.108    0.112
##     swesourceNA_0c   -0.002    0.076   -0.027    0.978   -0.152    0.148
##     fam3_0            0.179    0.025    7.231    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_12c        0.809    0.090    8.984    0.000    0.632    0.985
##    .mvpaagt_24      255.947   31.903    8.023    0.000  193.418  318.475
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     wieswe_0c         1.007    0.092   10.943    0.000    0.827    1.188
##     mvpaagt_0c      816.487   75.955   10.750    0.000  667.618  965.355
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.964    0.365   10.866    0.000    3.249    4.679
##     poshee_0c         0.758    0.069   10.944    0.000    0.622    0.894
##     swesourceNA_0c    1.349    0.125   10.754    0.000    1.103    1.594
##     fam3_0            0.147    0.013   10.956    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.736    0.585    1.258    0.208   -0.411    1.882
##     total             2.836    2.972    0.954    0.340   -2.990    8.661

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                     est      se     R     2.5%    97.5%
## a1                               0.2639  0.1466 20000  -0.0224   0.5519
## a2                               0.1877  0.0777 20000   0.0350   0.3402
## a3                               0.0010  0.0027 20000  -0.0044   0.0063
## a4                              -0.1127  0.1766 20000  -0.4587   0.2355
## a5                              -0.1502  0.1790 20000  -0.5025   0.2015
## a6                               0.0151  0.1501 20000  -0.2769   0.3052
## a7                              -0.0086  0.0104 20000  -0.0292   0.0113
## a8                              -0.0230  0.0157 20000  -0.0539   0.0077
## a9                               0.0176  0.0392 20000  -0.0601   0.0930
## a10                              0.0414  0.0888 20000  -0.1327   0.2165
## a11                             -0.1836  0.0736 20000  -0.3271  -0.0394
## a12                              0.1462  0.1892 20000  -0.2219   0.5200
## c1                               2.0997  2.9927 20000  -3.9235   7.8810
## c2                              -0.3205  1.5153 20000  -3.2695   2.6918
## c3                               0.7257  0.0562 20000   0.6149   0.8350
## c4                              -2.3606  3.5325 20000  -9.2440   4.6237
## c5                               4.4361  3.6450 20000  -2.7909  11.5615
## c6                              -1.5156  2.9164 20000  -7.1230   4.3217
## c7                              -0.1898  0.2173 20000  -0.6183   0.2336
## c8                              -0.2068  0.3421 20000  -0.8763   0.4647
## c9                              -1.8914  0.8232 20000  -3.5061  -0.2928
## c10                              0.4997  1.7083 20000  -2.8960   3.8319
## c11                             -2.1521  1.5346 20000  -5.1218   0.8799
## c12                              4.0893  3.7771 20000  -3.2743  11.5081
## b1                               2.7887  1.5906 20000  -0.3703   5.8867
## groupfu~~wieswe_0c              -0.0630  0.0326 20000  -0.1267   0.0020
## groupfu~~mvpaagt_0c             -0.7542  0.9335 20000  -2.5813   1.0679
## groupfu~~HIE                     0.0069  0.0151 20000  -0.0228   0.0365
## groupfu~~LIE                    -0.0035  0.0152 20000  -0.0333   0.0266
## groupfu~~sex_0                   0.0003  0.0156 20000  -0.0306   0.0302
## groupfu~~age_0c                 -0.0706  0.2458 20000  -0.5535   0.4134
## groupfu~~bmi_0c                 -0.0398  0.1561 20000  -0.3455   0.2655
## groupfu~~painvas_0c             -0.1505  0.0659 20000  -0.2796  -0.0203
## groupfu~~poshee_0c               0.0040  0.0281 20000  -0.0515   0.0581
## groupfu~~swesourceNA_0c          0.0672  0.0384 20000  -0.0078   0.1421
## groupfu~~fam3_0                 -0.0327  0.0127 20000  -0.0576  -0.0082
## wieswe_0c~~mvpaagt_0c            3.7848  1.8952 20000   0.0856   7.5150
## wieswe_0c~~HIE                   0.0202  0.0304 20000  -0.0390   0.0798
## wieswe_0c~~LIE                   0.0162  0.0307 20000  -0.0426   0.0763
## wieswe_0c~~sex_0                -0.0059  0.0315 20000  -0.0678   0.0554
## wieswe_0c~~age_0c               -0.9576  0.4951 20000  -1.9237   0.0213
## wieswe_0c~~bmi_0c               -0.8156  0.3189 20000  -1.4439  -0.1917
## wieswe_0c~~painvas_0c           -0.0533  0.1306 20000  -0.3141   0.2006
## wieswe_0c~~poshee_0c             0.2717  0.0593 20000   0.1569   0.3889
## wieswe_0c~~swesourceNA_0c       -0.2187  0.0770 20000  -0.3689  -0.0688
## wieswe_0c~~fam3_0                0.0205  0.0250 20000  -0.0289   0.0695
## mvpaagt_0c~~HIE                 -0.8405  0.8936 20000  -2.5914   0.9107
## mvpaagt_0c~~LIE                  0.1284  0.8829 20000  -1.6008   1.8584
## mvpaagt_0c~~sex_0                0.8196  0.9132 20000  -0.9861   2.6150
## mvpaagt_0c~~age_0c             -51.6872 14.4669 20000 -79.9431 -22.9599
## mvpaagt_0c~~bmi_0c             -23.5566  9.2877 20000 -41.8989  -5.5328
## mvpaagt_0c~~painvas_0c           6.6165  3.7836 20000  -0.9481  13.9252
## mvpaagt_0c~~poshee_0c            2.6890  1.6482 20000  -0.5400   5.9435
## mvpaagt_0c~~swesourceNA_0c       1.5639  2.2104 20000  -2.8034   5.8434
## mvpaagt_0c~~fam3_0               0.2839  0.7095 20000  -1.0973   1.6639
## HIE~~LIE                        -0.1144  0.0160 20000  -0.1458  -0.0825
## HIE~~sex_0                      -0.0010  0.0147 20000  -0.0298   0.0278
## HIE~~age_0c                      0.1878  0.2329 20000  -0.2673   0.6446
## HIE~~bmi_0c                      0.3432  0.1488 20000   0.0501   0.6304
## HIE~~painvas_0c                 -0.0257  0.0609 20000  -0.1458   0.0922
## HIE~~poshee_0c                   0.0453  0.0269 20000  -0.0076   0.0975
## HIE~~swesourceNA_0c             -0.0354  0.0361 20000  -0.1055   0.0359
## HIE~~fam3_0                      0.0021  0.0117 20000  -0.0210   0.0249
## LIE~~sex_0                       0.0016  0.0149 20000  -0.0275   0.0305
## LIE~~age_0c                     -0.2255  0.2341 20000  -0.6878   0.2339
## LIE~~bmi_0c                     -0.3705  0.1504 20000  -0.6690  -0.0741
## LIE~~painvas_0c                  0.0400  0.0610 20000  -0.0778   0.1605
## LIE~~poshee_0c                  -0.0404  0.0269 20000  -0.0926   0.0121
## LIE~~swesourceNA_0c              0.0094  0.0362 20000  -0.0614   0.0802
## LIE~~fam3_0                     -0.0064  0.0117 20000  -0.0293   0.0167
## sex_0~~age_0c                    0.0533  0.2377 20000  -0.4167   0.5180
## sex_0~~bmi_0c                   -0.1011  0.1526 20000  -0.4034   0.2024
## sex_0~~painvas_0c               -0.0084  0.0626 20000  -0.1308   0.1142
## sex_0~~poshee_0c                -0.0344  0.0272 20000  -0.0875   0.0189
## sex_0~~swesourceNA_0c           -0.0727  0.0373 20000  -0.1467  -0.0009
## sex_0~~fam3_0                   -0.0253  0.0121 20000  -0.0495  -0.0020
## age_0c~~bmi_0c                  -1.6174  2.4084 20000  -6.3523   3.0756
## age_0c~~painvas_0c              -2.7382  0.9936 20000  -4.6823  -0.7907
## age_0c~~poshee_0c               -1.2980  0.4351 20000  -2.1550  -0.4459
## age_0c~~swesourceNA_0c          -1.1078  0.5809 20000  -2.2380   0.0171
## age_0c~~fam3_0                  -0.0283  0.1885 20000  -0.3978   0.3403
## bmi_0c~~painvas_0c               1.7993  0.6394 20000   0.5427   3.0543
## bmi_0c~~poshee_0c                0.0830  0.2727 20000  -0.4540   0.6142
## bmi_0c~~swesourceNA_0c           0.6161  0.3745 20000  -0.1127   1.3537
## bmi_0c~~fam3_0                   0.0556  0.1213 20000  -0.1822   0.2928
## painvas_0c~~poshee_0c           -0.0280  0.1201 20000  -0.2673   0.2059
## painvas_0c~~swesourceNA_0c       0.3488  0.1537 20000   0.0499   0.6495
## painvas_0c~~fam3_0               0.0671  0.0507 20000  -0.0320   0.1654
## poshee_0c~~swesourceNA_0c       -0.1260  0.0670 20000  -0.2581   0.0038
## poshee_0c~~fam3_0                0.0024  0.0215 20000  -0.0391   0.0453
## swesourceNA_0c~~fam3_0           0.0065  0.0291 20000  -0.0511   0.0642
## wieswe_12c~~wieswe_12c           0.8087  0.0898 20000   0.6319   0.9825
## mvpaagt_24~~mvpaagt_24         255.9466 31.9633 20000 194.1335 319.0844
## groupfu~~groupfu                 0.2499  0.0229 20000   0.2056   0.2950
## wieswe_0c~~wieswe_0c             1.0074  0.0922 20000   0.8300   1.1898
## mvpaagt_0c~~mvpaagt_0c         816.4866 75.7136 20000 670.1253 965.9327
## HIE~~HIE                         0.2231  0.0202 20000   0.1835   0.2624
## LIE~~LIE                         0.2245  0.0203 20000   0.1846   0.2646
## sex_0~~sex_0                     0.2340  0.0211 20000   0.1929   0.2751
## age_0c~~age_0c                  57.7334  5.2414 20000  47.5510  68.0533
## bmi_0c~~bmi_0c                  23.6379  2.1543 20000  19.4650  27.8863
## painvas_0c~~painvas_0c           3.9644  0.3625 20000   3.2610   4.6893
## poshee_0c~~poshee_0c             0.7581  0.0692 20000   0.6207   0.8926
## swesourceNA_0c~~swesourceNA_0c   1.3485  0.1257 20000   1.1008   1.5941
## fam3_0~~fam3_0                   0.1470  0.0134 20000   0.1208   0.1733
## wieswe_12c~1                    -0.0652  0.1549 20000  -0.3685   0.2351
## mvpaagt_24~1                    35.9815  3.2384 20000  29.7057  42.4163
## groupfu~1                        0.5104  0.0321 20000   0.4478   0.5738
## wieswe_0c~1                      0.0029  0.0651 20000  -0.1244   0.1295
## mvpaagt_0c~1                    -0.3108  1.8801 20000  -4.0523   3.3522
## HIE~1                            0.3361  0.0305 20000   0.2768   0.3968
## LIE~1                            0.3402  0.0305 20000   0.2804   0.4006
## sex_0~1                          0.3734  0.0312 20000   0.3126   0.4339
## age_0c~1                         0.0000  0.4899 20000  -0.9573   0.9584
## bmi_0c~1                         0.0000  0.3139 20000  -0.6046   0.6218
## painvas_0c~1                    -0.0038  0.1297 20000  -0.2590   0.2534
## poshee_0c~1                      0.0023  0.0561 20000  -0.1074   0.1132
## swesourceNA_0c~1                -0.0021  0.0763 20000  -0.1538   0.1482
## fam3_0~1                         0.1790  0.0248 20000   0.1311   0.2280
## ind                              0.7358  0.6266 20000  -0.1869   2.2281
## total                            2.8355  2.9879 20000  -3.2080   8.5407

Recovery Self-Efficacy at 18 Months

model <- '
# Direct Effects
wieswe_18c ~ a1*groupfu + a2*wieswe_0c + a3*mvpaagt_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
mvpaagt_24 ~ c1*groupfu + c2*wieswe_0c + c3*mvpaagt_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*wieswe_18c

# Covariances
groupfu ~~ wieswe_0c + mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
wieswe_0c ~~ mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
mvpaagt_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 403 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        16
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wieswe_18c ~                                                          
##     groupfu   (a1)   -0.061    0.163   -0.374    0.708   -0.381    0.259
##     wiesw_0c  (a2)    0.173    0.089    1.947    0.052   -0.001    0.347
##     mvpgt_0c  (a3)    0.004    0.003    1.370    0.171   -0.002    0.010
##     HIE       (a4)    0.302    0.197    1.534    0.125   -0.084    0.687
##     LIE       (a5)    0.073    0.199    0.369    0.712   -0.317    0.464
##     sex_0     (a6)    0.004    0.167    0.023    0.982   -0.323    0.330
##     age_0c    (a7)   -0.000    0.012   -0.015    0.988   -0.024    0.023
##     bmi_0c    (a8)   -0.023    0.018   -1.311    0.190   -0.058    0.012
##     panvs_0c  (a9)    0.038    0.045    0.841    0.400   -0.050    0.125
##     poshe_0c (a10)    0.168    0.096    1.758    0.079   -0.019    0.356
##     swsrNA_0 (a11)   -0.172    0.086   -1.988    0.047   -0.341   -0.002
##     fam3_0   (a12)    0.023    0.208    0.111    0.912   -0.385    0.431
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    3.012    2.959    1.018    0.309   -2.788    8.811
##     wiesw_0c  (c2)   -0.129    1.519   -0.085    0.932   -3.106    2.848
##     mvpgt_0c  (c3)    0.723    0.057   12.680    0.000    0.611    0.835
##     HIE       (c4)   -3.232    3.538   -0.914    0.361  -10.167    3.703
##     LIE       (c5)    3.833    3.631    1.056    0.291   -3.283   10.949
##     sex_0     (c6)   -1.562    2.956   -0.528    0.597   -7.355    4.231
##     age_0c    (c7)   -0.211    0.218   -0.971    0.332   -0.638    0.215
##     bmi_0c    (c8)   -0.205    0.343   -0.597    0.551   -0.878    0.468
##     panvs_0c  (c9)   -1.883    0.828   -2.274    0.023   -3.507   -0.260
##     poshe_0c (c10)    0.284    1.721    0.165    0.869   -3.090    3.657
##     swsrNA_0 (c11)   -2.516    1.505   -1.671    0.095   -5.466    0.434
##     fam3_0   (c12)    4.538    3.837    1.183    0.237   -2.982   12.058
##     wisw_18c  (b1)    1.955    1.549    1.262    0.207   -1.081    4.992
## 
## Covariances:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                             
##     wieswe_0c         -0.063    0.033   -1.932    0.053   -0.127    0.001
##     mvpaagt_0c        -0.753    0.936   -0.805    0.421   -2.587    1.080
##     HIE                0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE               -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0              0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c            -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c            -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c        -0.151    0.065   -2.301    0.021   -0.279   -0.022
##     poshee_0c          0.004    0.028    0.150    0.881   -0.051    0.059
##     swesourceNA_0c     0.066    0.038    1.719    0.086   -0.009    0.141
##     fam3_0            -0.033    0.013   -2.606    0.009   -0.057   -0.008
##   wieswe_0c ~~                                                           
##     mvpaagt_0c         3.771    1.884    2.001    0.045    0.078    7.464
##     HIE                0.020    0.031    0.661    0.509   -0.040    0.080
##     LIE                0.016    0.031    0.528    0.597   -0.044    0.077
##     sex_0             -0.006    0.031   -0.189    0.850   -0.067    0.056
##     age_0c            -0.958    0.495   -1.934    0.053   -1.928    0.013
##     bmi_0c            -0.816    0.319   -2.554    0.011   -1.442   -0.190
##     painvas_0c        -0.053    0.131   -0.406    0.684   -0.309    0.203
##     poshee_0c          0.272    0.059    4.596    0.000    0.156    0.388
##     swesourceNA_0c    -0.218    0.077   -2.823    0.005   -0.369   -0.067
##     fam3_0             0.020    0.025    0.824    0.410   -0.028    0.069
##   mvpaagt_0c ~~                                                          
##     HIE               -0.842    0.887   -0.950    0.342   -2.582    0.897
##     LIE                0.135    0.883    0.152    0.879   -1.596    1.866
##     sex_0              0.817    0.909    0.899    0.369   -0.964    2.598
##     age_0c           -51.646   14.503   -3.561    0.000  -80.070  -23.221
##     bmi_0c           -23.452    9.259   -2.533    0.011  -41.599   -5.305
##     painvas_0c         6.563    3.797    1.729    0.084   -0.879   14.005
##     poshee_0c          2.687    1.642    1.637    0.102   -0.531    5.905
##     swesourceNA_0c     1.570    2.206    0.712    0.477   -2.753    5.893
##     fam3_0             0.281    0.708    0.396    0.692   -1.108    1.669
##   HIE ~~                                                                 
##     LIE               -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0             -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c             0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c             0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c        -0.026    0.061   -0.420    0.675   -0.145    0.094
##     poshee_0c          0.046    0.027    1.702    0.089   -0.007    0.098
##     swesourceNA_0c    -0.036    0.036   -0.999    0.318   -0.107    0.035
##     fam3_0             0.002    0.012    0.179    0.858   -0.021    0.025
##   LIE ~~                                                                 
##     sex_0              0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c            -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c            -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c         0.039    0.062    0.627    0.531   -0.082    0.159
##     poshee_0c         -0.040    0.027   -1.515    0.130   -0.093    0.012
##     swesourceNA_0c     0.011    0.036    0.295    0.768   -0.060    0.081
##     fam3_0            -0.006    0.012   -0.545    0.586   -0.029    0.017
##   sex_0 ~~                                                               
##     age_0c             0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c            -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c        -0.008    0.063   -0.135    0.893   -0.131    0.114
##     poshee_0c         -0.034    0.027   -1.265    0.206   -0.088    0.019
##     swesourceNA_0c    -0.072    0.037   -1.947    0.052   -0.145    0.000
##     fam3_0            -0.025    0.012   -2.099    0.036   -0.049   -0.002
##   age_0c ~~                                                              
##     bmi_0c            -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c        -2.732    0.995   -2.746    0.006   -4.683   -0.782
##     poshee_0c         -1.297    0.434   -2.986    0.003   -2.149   -0.446
##     swesourceNA_0c    -1.105    0.581   -1.904    0.057   -2.243    0.033
##     fam3_0            -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                              
##     painvas_0c         1.800    0.642    2.805    0.005    0.542    3.057
##     poshee_0c          0.086    0.274    0.313    0.754   -0.452    0.623
##     swesourceNA_0c     0.601    0.374    1.606    0.108   -0.132    1.334
##     fam3_0             0.056    0.121    0.461    0.644   -0.181    0.292
##   painvas_0c ~~                                                          
##     poshee_0c         -0.033    0.120   -0.278    0.781   -0.269    0.202
##     swesourceNA_0c     0.346    0.153    2.260    0.024    0.046    0.646
##     fam3_0             0.066    0.051    1.299    0.194   -0.034    0.165
##   poshee_0c ~~                                                           
##     swesourceNA_0c    -0.127    0.067   -1.897    0.058   -0.257    0.004
##     fam3_0             0.002    0.022    0.110    0.912   -0.040    0.045
##   swesourceNA_0c ~~                                                      
##     fam3_0             0.006    0.029    0.212    0.832   -0.051    0.064
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_18c       -0.089    0.174   -0.512    0.609   -0.429    0.251
##    .mvpaagt_24       36.033    3.265   11.036    0.000   29.633   42.432
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     wieswe_0c         0.003    0.065    0.045    0.964   -0.124    0.130
##     mvpaagt_0c       -0.294    1.870   -0.157    0.875   -3.958    3.371
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c            0.000    0.489    0.000    1.000   -0.959    0.959
##     bmi_0c           -0.000    0.313   -0.000    1.000   -0.614    0.614
##     painvas_0c       -0.004    0.130   -0.029    0.977   -0.258    0.250
##     poshee_0c         0.003    0.056    0.048    0.962   -0.107    0.113
##     swesourceNA_0c   -0.001    0.076   -0.008    0.994   -0.150    0.149
##     fam3_0            0.179    0.025    7.231    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_18c        0.930    0.108    8.632    0.000    0.719    1.141
##    .mvpaagt_24      258.600   32.233    8.023    0.000  195.424  321.776
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     wieswe_0c         1.007    0.092   10.943    0.000    0.827    1.188
##     mvpaagt_0c      816.252   75.924   10.751    0.000  667.443  965.061
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.962    0.364   10.872    0.000    3.248    4.676
##     poshee_0c         0.758    0.069   10.940    0.000    0.623    0.894
##     swesourceNA_0c    1.348    0.125   10.757    0.000    1.102    1.594
##     fam3_0            0.147    0.013   10.956    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.119    0.329   -0.363    0.717   -0.764    0.525
##     total             2.892    2.975    0.972    0.331   -2.938    8.723

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                     est      se     R     2.5%    97.5%
## a1                              -0.0610  0.1630 20000  -0.3795   0.2542
## a2                               0.1729  0.0885 20000  -0.0007   0.3450
## a3                               0.0041  0.0030 20000  -0.0017   0.0100
## a4                               0.3017  0.1973 20000  -0.0821   0.6885
## a5                               0.0735  0.2000 20000  -0.3208   0.4707
## a6                               0.0038  0.1670 20000  -0.3222   0.3308
## a7                              -0.0002  0.0121 20000  -0.0237   0.0237
## a8                              -0.0233  0.0178 20000  -0.0583   0.0115
## a9                               0.0376  0.0447 20000  -0.0497   0.1251
## a10                              0.1683  0.0945 20000  -0.0147   0.3542
## a11                             -0.1719  0.0864 20000  -0.3387   0.0003
## a12                              0.0231  0.2096 20000  -0.3906   0.4341
## c1                               3.0117  2.9649 20000  -2.7248   8.8881
## c2                              -0.1289  1.5157 20000  -3.1102   2.8814
## c3                               0.7233  0.0569 20000   0.6128   0.8360
## c4                              -3.2322  3.5321 20000 -10.2104   3.7458
## c5                               3.8326  3.6383 20000  -3.2350  11.1624
## c6                              -1.5619  2.9138 20000  -7.3601   4.0145
## c7                              -0.2113  0.2181 20000  -0.6456   0.2133
## c8                              -0.2049  0.3429 20000  -0.8749   0.4607
## c9                              -1.8834  0.8377 20000  -3.5234  -0.2447
## c10                              0.2836  1.7334 20000  -3.1296   3.6583
## c11                             -2.5157  1.5058 20000  -5.4531   0.4398
## c12                              4.5377  3.8159 20000  -2.9137  12.0094
## b1                               1.9554  1.5568 20000  -1.1155   4.9807
## groupfu~~wieswe_0c              -0.0630  0.0328 20000  -0.1276   0.0010
## groupfu~~mvpaagt_0c             -0.7533  0.9381 20000  -2.5645   1.0711
## groupfu~~HIE                     0.0069  0.0152 20000  -0.0228   0.0369
## groupfu~~LIE                    -0.0035  0.0152 20000  -0.0329   0.0262
## groupfu~~sex_0                   0.0003  0.0157 20000  -0.0303   0.0312
## groupfu~~age_0c                 -0.0706  0.2437 20000  -0.5471   0.4045
## groupfu~~bmi_0c                 -0.0398  0.1574 20000  -0.3496   0.2655
## groupfu~~painvas_0c             -0.1505  0.0660 20000  -0.2817  -0.0230
## groupfu~~poshee_0c               0.0042  0.0280 20000  -0.0513   0.0584
## groupfu~~swesourceNA_0c          0.0661  0.0384 20000  -0.0094   0.1412
## groupfu~~fam3_0                 -0.0327  0.0125 20000  -0.0571  -0.0081
## wieswe_0c~~mvpaagt_0c            3.7709  1.8818 20000  -0.0193   7.4381
## wieswe_0c~~HIE                   0.0202  0.0305 20000  -0.0393   0.0803
## wieswe_0c~~LIE                   0.0162  0.0308 20000  -0.0441   0.0764
## wieswe_0c~~sex_0                -0.0059  0.0314 20000  -0.0674   0.0551
## wieswe_0c~~age_0c               -0.9575  0.4951 20000  -1.9179   0.0188
## wieswe_0c~~bmi_0c               -0.8156  0.3195 20000  -1.4451  -0.1925
## wieswe_0c~~painvas_0c           -0.0531  0.1298 20000  -0.3070   0.1981
## wieswe_0c~~poshee_0c             0.2721  0.0589 20000   0.1570   0.3873
## wieswe_0c~~swesourceNA_0c       -0.2178  0.0772 20000  -0.3693  -0.0678
## wieswe_0c~~fam3_0                0.0205  0.0247 20000  -0.0286   0.0688
## mvpaagt_0c~~HIE                 -0.8425  0.8873 20000  -2.5659   0.8996
## mvpaagt_0c~~LIE                  0.1346  0.8808 20000  -1.5963   1.8418
## mvpaagt_0c~~sex_0                0.8169  0.9106 20000  -0.9601   2.5992
## mvpaagt_0c~~age_0c             -51.6458 14.4722 20000 -79.9231 -22.8725
## mvpaagt_0c~~bmi_0c             -23.4520  9.2510 20000 -41.3546  -5.2916
## mvpaagt_0c~~painvas_0c           6.5630  3.7601 20000  -0.7279  14.0386
## mvpaagt_0c~~poshee_0c            2.6873  1.6412 20000  -0.5777   5.9204
## mvpaagt_0c~~swesourceNA_0c       1.5700  2.2214 20000  -2.7672   5.9571
## mvpaagt_0c~~fam3_0               0.2808  0.7121 20000  -1.1253   1.6685
## HIE~~LIE                        -0.1144  0.0163 20000  -0.1460  -0.0825
## HIE~~sex_0                      -0.0010  0.0147 20000  -0.0296   0.0277
## HIE~~age_0c                      0.1878  0.2332 20000  -0.2660   0.6434
## HIE~~bmi_0c                      0.3432  0.1489 20000   0.0474   0.6330
## HIE~~painvas_0c                 -0.0256  0.0614 20000  -0.1473   0.0931
## HIE~~poshee_0c                   0.0455  0.0265 20000  -0.0068   0.0972
## HIE~~swesourceNA_0c             -0.0362  0.0361 20000  -0.1069   0.0344
## HIE~~fam3_0                      0.0021  0.0117 20000  -0.0209   0.0250
## LIE~~sex_0                       0.0016  0.0146 20000  -0.0270   0.0302
## LIE~~age_0c                     -0.2255  0.2340 20000  -0.6840   0.2293
## LIE~~bmi_0c                     -0.3705  0.1498 20000  -0.6611  -0.0779
## LIE~~painvas_0c                  0.0386  0.0616 20000  -0.0821   0.1609
## LIE~~poshee_0c                  -0.0405  0.0266 20000  -0.0929   0.0114
## LIE~~swesourceNA_0c              0.0106  0.0362 20000  -0.0606   0.0814
## LIE~~fam3_0                     -0.0064  0.0117 20000  -0.0295   0.0163
## sex_0~~age_0c                    0.0533  0.2364 20000  -0.4051   0.5204
## sex_0~~bmi_0c                   -0.1011  0.1529 20000  -0.3998   0.1975
## sex_0~~painvas_0c               -0.0085  0.0630 20000  -0.1305   0.1142
## sex_0~~poshee_0c                -0.0345  0.0272 20000  -0.0876   0.0187
## sex_0~~swesourceNA_0c           -0.0723  0.0371 20000  -0.1452   0.0002
## sex_0~~fam3_0                   -0.0253  0.0120 20000  -0.0491  -0.0022
## age_0c~~bmi_0c                  -1.6174  2.4058 20000  -6.3165   3.1497
## age_0c~~painvas_0c              -2.7324  0.9876 20000  -4.6690  -0.7996
## age_0c~~poshee_0c               -1.2972  0.4352 20000  -2.1490  -0.4457
## age_0c~~swesourceNA_0c          -1.1051  0.5821 20000  -2.2417   0.0189
## age_0c~~fam3_0                  -0.0283  0.1882 20000  -0.4002   0.3381
## bmi_0c~~painvas_0c               1.7995  0.6424 20000   0.5310   3.0594
## bmi_0c~~poshee_0c                0.0859  0.2739 20000  -0.4490   0.6307
## bmi_0c~~swesourceNA_0c           0.6009  0.3726 20000  -0.1271   1.3413
## bmi_0c~~fam3_0                   0.0556  0.1205 20000  -0.1804   0.2938
## painvas_0c~~poshee_0c           -0.0335  0.1208 20000  -0.2688   0.2009
## painvas_0c~~swesourceNA_0c       0.3460  0.1538 20000   0.0459   0.6491
## painvas_0c~~fam3_0               0.0660  0.0509 20000  -0.0332   0.1658
## poshee_0c~~swesourceNA_0c       -0.1266  0.0665 20000  -0.2586   0.0023
## poshee_0c~~fam3_0                0.0024  0.0215 20000  -0.0399   0.0443
## swesourceNA_0c~~fam3_0           0.0062  0.0295 20000  -0.0513   0.0644
## wieswe_18c~~wieswe_18c           0.9300  0.1087 20000   0.7206   1.1437
## mvpaagt_24~~mvpaagt_24         258.6001 32.2944 20000 196.1708 322.4018
## groupfu~~groupfu                 0.2499  0.0228 20000   0.2063   0.2956
## wieswe_0c~~wieswe_0c             1.0074  0.0918 20000   0.8288   1.1910
## mvpaagt_0c~~mvpaagt_0c         816.2521 75.6877 20000 669.9786 965.5088
## HIE~~HIE                         0.2231  0.0202 20000   0.1835   0.2628
## LIE~~LIE                         0.2245  0.0206 20000   0.1842   0.2645
## sex_0~~sex_0                     0.2340  0.0212 20000   0.1922   0.2759
## age_0c~~age_0c                  57.7334  5.2397 20000  47.5590  68.0426
## bmi_0c~~bmi_0c                  23.6379  2.1532 20000  19.3649  27.7921
## painvas_0c~~painvas_0c           3.9617  0.3629 20000   3.2557   4.6724
## poshee_0c~~poshee_0c             0.7584  0.0694 20000   0.6253   0.8951
## swesourceNA_0c~~swesourceNA_0c   1.3481  0.1250 20000   1.1090   1.5935
## fam3_0~~fam3_0                   0.1470  0.0135 20000   0.1208   0.1738
## wieswe_18c~1                    -0.0888  0.1750 20000  -0.4331   0.2550
## mvpaagt_24~1                    36.0326  3.2530 20000  29.6040  42.3449
## groupfu~1                        0.5104  0.0320 20000   0.4480   0.5735
## wieswe_0c~1                      0.0029  0.0651 20000  -0.1241   0.1306
## mvpaagt_0c~1                    -0.2935  1.8810 20000  -3.9342   3.4197
## HIE~1                            0.3361  0.0304 20000   0.2766   0.3959
## LIE~1                            0.3402  0.0305 20000   0.2805   0.4003
## sex_0~1                          0.3734  0.0311 20000   0.3127   0.4343
## age_0c~1                         0.0000  0.4900 20000  -0.9615   0.9606
## bmi_0c~1                         0.0000  0.3137 20000  -0.6194   0.6029
## painvas_0c~1                    -0.0037  0.1301 20000  -0.2599   0.2479
## poshee_0c~1                      0.0027  0.0569 20000  -0.1070   0.1154
## swesourceNA_0c~1                -0.0006  0.0757 20000  -0.1502   0.1473
## fam3_0~1                         0.1790  0.0249 20000   0.1307   0.2282
## ind                             -0.1193  0.4156 20000  -1.0835   0.6906
## total                            2.8924  2.9879 20000  -2.8355   8.8492

Action Control at 12 Months

model <- '
# Direct Effects
hk_12c ~ a1*groupfu + a2*hk_0c + a3*mvpaagt_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
mvpaagt_24 ~ c1*groupfu + c2*hk_0c + c3*mvpaagt_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*hk_12c

# Covariances
groupfu ~~ hk_0c + mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
hk_0c ~~ mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
mvpaagt_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 418 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        15
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   hk_12c ~                                                              
##     groupfu   (a1)    0.331    0.177    1.865    0.062   -0.017    0.678
##     hk_0c     (a2)    0.345    0.065    5.295    0.000    0.217    0.472
##     mvpgt_0c  (a3)    0.002    0.003    0.498    0.619   -0.005    0.008
##     HIE       (a4)    0.208    0.214    0.973    0.331   -0.211    0.626
##     LIE       (a5)    0.416    0.217    1.917    0.055   -0.009    0.841
##     sex_0     (a6)    0.111    0.181    0.611    0.541   -0.245    0.466
##     age_0c    (a7)    0.037    0.013    2.800    0.005    0.011    0.062
##     bmi_0c    (a8)    0.012    0.019    0.633    0.526   -0.025    0.049
##     panvs_0c  (a9)    0.034    0.047    0.709    0.478   -0.059    0.127
##     poshe_0c (a10)    0.035    0.108    0.326    0.744   -0.176    0.247
##     swsrNA_0 (a11)   -0.156    0.093   -1.686    0.092   -0.338    0.025
##     fam3_0   (a12)   -0.012    0.228   -0.053    0.958   -0.459    0.435
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    2.411    2.984    0.808    0.419   -3.438    8.260
##     hk_0c     (c2)    0.905    1.137    0.795    0.426   -1.325    3.134
##     mvpgt_0c  (c3)    0.719    0.056   12.785    0.000    0.609    0.830
##     HIE       (c4)   -3.837    3.537   -1.085    0.278  -10.769    3.096
##     LIE       (c5)    2.550    3.641    0.700    0.484   -4.587    9.687
##     sex_0     (c6)   -1.682    2.935   -0.573    0.567   -7.435    4.071
##     age_0c    (c7)   -0.408    0.235   -1.737    0.082   -0.869    0.052
##     bmi_0c    (c8)   -0.321    0.340   -0.945    0.345   -0.987    0.345
##     panvs_0c  (c9)   -2.080    0.830   -2.507    0.012   -3.707   -0.454
##     poshe_0c (c10)    0.148    1.656    0.089    0.929   -3.098    3.394
##     swsrNA_0 (c11)   -2.358    1.459   -1.616    0.106   -5.218    0.501
##     fam3_0   (c12)    4.886    3.815    1.281    0.200   -2.591   12.362
##     hk_12c    (b1)    2.031    1.399    1.452    0.147   -0.711    4.774
## 
## Covariances:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                             
##     hk_0c             -0.040    0.046   -0.874    0.382   -0.130    0.050
##     mvpaagt_0c        -0.736    0.935   -0.787    0.431   -2.569    1.096
##     HIE                0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE               -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0              0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c            -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c            -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c        -0.152    0.065   -2.323    0.020   -0.280   -0.024
##     poshee_0c          0.004    0.028    0.125    0.900   -0.052    0.059
##     swesourceNA_0c     0.071    0.039    1.840    0.066   -0.005    0.146
##     fam3_0            -0.033    0.013   -2.607    0.009   -0.057   -0.008
##   hk_0c ~~                                                               
##     mvpaagt_0c         2.421    2.675    0.905    0.365   -2.822    7.664
##     HIE                0.047    0.043    1.092    0.275   -0.038    0.132
##     LIE               -0.001    0.043   -0.013    0.989   -0.085    0.084
##     sex_0             -0.022    0.044   -0.490    0.624   -0.108    0.065
##     age_0c             2.159    0.709    3.047    0.002    0.770    3.548
##     bmi_0c            -0.668    0.447   -1.496    0.135   -1.544    0.207
##     painvas_0c        -0.067    0.184   -0.365    0.715   -0.428    0.293
##     poshee_0c          0.169    0.081    2.085    0.037    0.010    0.327
##     swesourceNA_0c    -0.132    0.109   -1.209    0.227   -0.346    0.082
##     fam3_0             0.003    0.035    0.074    0.941   -0.066    0.071
##   mvpaagt_0c ~~                                                          
##     HIE               -0.836    0.887   -0.942    0.346   -2.575    0.903
##     LIE                0.132    0.883    0.150    0.881   -1.598    1.863
##     sex_0              0.780    0.908    0.859    0.390   -1.000    2.560
##     age_0c           -51.241   14.488   -3.537    0.000  -79.636  -22.846
##     bmi_0c           -23.910    9.262   -2.581    0.010  -42.063   -5.756
##     painvas_0c         6.377    3.796    1.680    0.093   -1.063   13.816
##     poshee_0c          2.656    1.640    1.620    0.105   -0.558    5.870
##     swesourceNA_0c     1.452    2.209    0.657    0.511   -2.877    5.781
##     fam3_0             0.289    0.708    0.408    0.684   -1.099    1.676
##   HIE ~~                                                                 
##     LIE               -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0             -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c             0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c             0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c        -0.024    0.061   -0.399    0.690   -0.144    0.095
##     poshee_0c          0.045    0.027    1.667    0.096   -0.008    0.097
##     swesourceNA_0c    -0.034    0.036   -0.925    0.355   -0.105    0.038
##     fam3_0             0.002    0.012    0.180    0.857   -0.021    0.025
##   LIE ~~                                                                 
##     sex_0              0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c            -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c            -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c         0.037    0.062    0.607    0.544   -0.083    0.158
##     poshee_0c         -0.040    0.027   -1.498    0.134   -0.092    0.012
##     swesourceNA_0c     0.009    0.036    0.261    0.794   -0.061    0.080
##     fam3_0            -0.006    0.012   -0.546    0.585   -0.029    0.017
##   sex_0 ~~                                                               
##     age_0c             0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c            -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c        -0.008    0.063   -0.125    0.901   -0.131    0.115
##     poshee_0c         -0.034    0.027   -1.247    0.213   -0.087    0.019
##     swesourceNA_0c    -0.076    0.037   -2.049    0.040   -0.149   -0.003
##     fam3_0            -0.025    0.012   -2.098    0.036   -0.049   -0.002
##   age_0c ~~                                                              
##     bmi_0c            -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c        -2.736    0.995   -2.749    0.006   -4.687   -0.785
##     poshee_0c         -1.301    0.434   -2.995    0.003   -2.152   -0.450
##     swesourceNA_0c    -1.088    0.582   -1.870    0.061   -2.227    0.052
##     fam3_0            -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                              
##     painvas_0c         1.816    0.642    2.829    0.005    0.558    3.075
##     poshee_0c          0.074    0.274    0.270    0.787   -0.464    0.612
##     swesourceNA_0c     0.653    0.375    1.740    0.082   -0.083    1.388
##     fam3_0             0.055    0.121    0.460    0.645   -0.181    0.292
##   painvas_0c ~~                                                          
##     poshee_0c         -0.030    0.120   -0.252    0.801   -0.266    0.205
##     swesourceNA_0c     0.343    0.153    2.233    0.026    0.042    0.643
##     fam3_0             0.066    0.051    1.296    0.195   -0.034    0.165
##   poshee_0c ~~                                                           
##     swesourceNA_0c    -0.123    0.067   -1.840    0.066   -0.254    0.008
##     fam3_0             0.003    0.022    0.123    0.902   -0.040    0.045
##   swesourceNA_0c ~~                                                      
##     fam3_0             0.006    0.029    0.207    0.836   -0.051    0.064
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_12c           -0.453    0.188   -2.413    0.016   -0.822   -0.085
##    .mvpaagt_24       36.647    3.272   11.199    0.000   30.233   43.061
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     hk_0c            -0.000    0.091   -0.000    1.000   -0.179    0.179
##     mvpaagt_0c       -0.337    1.868   -0.180    0.857   -3.999    3.325
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c           -0.000    0.489   -0.000    1.000   -0.959    0.959
##     bmi_0c           -0.000    0.313   -0.000    1.000   -0.614    0.614
##     painvas_0c       -0.006    0.130   -0.043    0.966   -0.260    0.248
##     poshee_0c         0.001    0.056    0.022    0.982   -0.109    0.111
##     swesourceNA_0c    0.005    0.077    0.062    0.951   -0.145    0.155
##     fam3_0            0.179    0.025    7.230    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_12c            1.182    0.132    8.947    0.000    0.923    1.441
##    .mvpaagt_24      254.689   31.726    8.028    0.000  192.508  316.871
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     hk_0c             2.015    0.184   10.977    0.000    1.655    2.375
##     mvpaagt_0c      815.144   75.801   10.754    0.000  666.578  963.711
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.965    0.365   10.864    0.000    3.250    4.681
##     poshee_0c         0.757    0.069   10.953    0.000    0.622    0.893
##     swesourceNA_0c    1.352    0.126   10.725    0.000    1.105    1.599
##     fam3_0            0.147    0.013   10.956    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.672    0.587    1.144    0.253   -0.479    1.823
##     total             3.083    2.954    1.044    0.297   -2.706    8.872

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                     est      se     R     2.5%    97.5%
## a1                               0.3307  0.1786 20000  -0.0196   0.6814
## a2                               0.3447  0.0653 20000   0.2175   0.4720
## a3                               0.0017  0.0034 20000  -0.0050   0.0083
## a4                               0.2078  0.2151 20000  -0.2146   0.6330
## a5                               0.4159  0.2174 20000  -0.0190   0.8384
## a6                               0.1109  0.1817 20000  -0.2469   0.4717
## a7                               0.0366  0.0131 20000   0.0108   0.0623
## a8                               0.0120  0.0190 20000  -0.0254   0.0495
## a9                               0.0336  0.0472 20000  -0.0581   0.1270
## a10                              0.0352  0.1079 20000  -0.1769   0.2502
## a11                             -0.1561  0.0932 20000  -0.3400   0.0294
## a12                             -0.0120  0.2260 20000  -0.4566   0.4332
## c1                               2.4113  3.0106 20000  -3.5647   8.3261
## c2                               0.9048  1.1377 20000  -1.2939   3.1501
## c3                               0.7193  0.0561 20000   0.6086   0.8286
## c4                              -3.8367  3.5274 20000 -10.8321   2.9574
## c5                               2.5498  3.6412 20000  -4.7335   9.6340
## c6                              -1.6821  2.9054 20000  -7.2971   4.0962
## c7                              -0.4081  0.2360 20000  -0.8619   0.0560
## c8                              -0.3209  0.3412 20000  -0.9933   0.3365
## c9                              -2.0804  0.8316 20000  -3.7086  -0.4519
## c10                              0.1482  1.6514 20000  -3.0938   3.3583
## c11                             -2.3583  1.4644 20000  -5.2044   0.5084
## c12                              4.8856  3.7803 20000  -2.5661  12.3146
## b1                               2.0313  1.3982 20000  -0.6707   4.7793
## groupfu~~hk_0c                  -0.0400  0.0459 20000  -0.1288   0.0507
## groupfu~~mvpaagt_0c             -0.7360  0.9336 20000  -2.5698   1.0818
## groupfu~~HIE                     0.0069  0.0153 20000  -0.0232   0.0368
## groupfu~~LIE                    -0.0035  0.0153 20000  -0.0333   0.0269
## groupfu~~sex_0                   0.0003  0.0156 20000  -0.0301   0.0311
## groupfu~~age_0c                 -0.0706  0.2467 20000  -0.5545   0.4103
## groupfu~~bmi_0c                 -0.0398  0.1570 20000  -0.3472   0.2708
## groupfu~~painvas_0c             -0.1521  0.0657 20000  -0.2830  -0.0251
## groupfu~~poshee_0c               0.0035  0.0282 20000  -0.0514   0.0592
## groupfu~~swesourceNA_0c          0.0709  0.0386 20000  -0.0054   0.1470
## groupfu~~fam3_0                 -0.0327  0.0127 20000  -0.0574  -0.0076
## hk_0c~~mvpaagt_0c                2.4208  2.6813 20000  -3.0085   7.6119
## hk_0c~~HIE                       0.0473  0.0432 20000  -0.0381   0.1327
## hk_0c~~LIE                      -0.0006  0.0433 20000  -0.0862   0.0832
## hk_0c~~sex_0                    -0.0217  0.0444 20000  -0.1087   0.0639
## hk_0c~~age_0c                    2.1591  0.7114 20000   0.7586   3.5498
## hk_0c~~bmi_0c                   -0.6683  0.4473 20000  -1.5446   0.1921
## hk_0c~~painvas_0c               -0.0671  0.1834 20000  -0.4225   0.2923
## hk_0c~~poshee_0c                 0.1687  0.0808 20000   0.0101   0.3280
## hk_0c~~swesourceNA_0c           -0.1321  0.1093 20000  -0.3463   0.0845
## hk_0c~~fam3_0                    0.0026  0.0350 20000  -0.0660   0.0709
## mvpaagt_0c~~HIE                 -0.8357  0.8901 20000  -2.5931   0.9058
## mvpaagt_0c~~LIE                  0.1325  0.8851 20000  -1.6151   1.8538
## mvpaagt_0c~~sex_0                0.7800  0.9096 20000  -1.0021   2.5743
## mvpaagt_0c~~age_0c             -51.2409 14.4506 20000 -79.9704 -22.9733
## mvpaagt_0c~~bmi_0c             -23.9096  9.2911 20000 -41.9452  -5.5636
## mvpaagt_0c~~painvas_0c           6.3768  3.7835 20000  -0.9004  13.9038
## mvpaagt_0c~~poshee_0c            2.6559  1.6476 20000  -0.5736   5.8826
## mvpaagt_0c~~swesourceNA_0c       1.4517  2.2169 20000  -2.8970   5.8080
## mvpaagt_0c~~fam3_0               0.2886  0.7123 20000  -1.0909   1.6792
## HIE~~LIE                        -0.1144  0.0163 20000  -0.1461  -0.0823
## HIE~~sex_0                      -0.0010  0.0147 20000  -0.0296   0.0280
## HIE~~age_0c                      0.1878  0.2334 20000  -0.2687   0.6428
## HIE~~bmi_0c                      0.3432  0.1487 20000   0.0512   0.6357
## HIE~~painvas_0c                 -0.0244  0.0610 20000  -0.1449   0.0941
## HIE~~poshee_0c                   0.0446  0.0269 20000  -0.0081   0.0971
## HIE~~swesourceNA_0c             -0.0336  0.0365 20000  -0.1054   0.0375
## HIE~~fam3_0                      0.0021  0.0117 20000  -0.0210   0.0249
## LIE~~sex_0                       0.0016  0.0147 20000  -0.0268   0.0305
## LIE~~age_0c                     -0.2255  0.2332 20000  -0.6912   0.2315
## LIE~~bmi_0c                     -0.3705  0.1523 20000  -0.6694  -0.0731
## LIE~~painvas_0c                  0.0374  0.0615 20000  -0.0822   0.1589
## LIE~~poshee_0c                  -0.0400  0.0269 20000  -0.0921   0.0134
## LIE~~swesourceNA_0c              0.0095  0.0361 20000  -0.0610   0.0811
## LIE~~fam3_0                     -0.0064  0.0117 20000  -0.0293   0.0168
## sex_0~~age_0c                    0.0533  0.2368 20000  -0.4106   0.5140
## sex_0~~bmi_0c                   -0.1011  0.1516 20000  -0.3976   0.1939
## sex_0~~painvas_0c               -0.0078  0.0627 20000  -0.1295   0.1147
## sex_0~~poshee_0c                -0.0340  0.0271 20000  -0.0872   0.0192
## sex_0~~swesourceNA_0c           -0.0763  0.0374 20000  -0.1503  -0.0030
## sex_0~~fam3_0                   -0.0253  0.0121 20000  -0.0491  -0.0014
## age_0c~~bmi_0c                  -1.6174  2.3944 20000  -6.2889   3.0772
## age_0c~~painvas_0c              -2.7361  0.9947 20000  -4.6782  -0.7756
## age_0c~~poshee_0c               -1.3006  0.4342 20000  -2.1479  -0.4382
## age_0c~~swesourceNA_0c          -1.0877  0.5833 20000  -2.2207   0.0604
## age_0c~~fam3_0                  -0.0283  0.1885 20000  -0.4016   0.3404
## bmi_0c~~painvas_0c               1.8165  0.6490 20000   0.5425   3.0913
## bmi_0c~~poshee_0c                0.0740  0.2741 20000  -0.4649   0.6123
## bmi_0c~~swesourceNA_0c           0.6529  0.3705 20000  -0.0799   1.3748
## bmi_0c~~fam3_0                   0.0555  0.1198 20000  -0.1801   0.2910
## painvas_0c~~poshee_0c           -0.0303  0.1208 20000  -0.2703   0.2087
## painvas_0c~~swesourceNA_0c       0.3426  0.1542 20000   0.0421   0.6469
## painvas_0c~~fam3_0               0.0658  0.0509 20000  -0.0342   0.1651
## poshee_0c~~swesourceNA_0c       -0.1229  0.0670 20000  -0.2527   0.0062
## poshee_0c~~fam3_0                0.0026  0.0215 20000  -0.0396   0.0443
## swesourceNA_0c~~fam3_0           0.0061  0.0292 20000  -0.0515   0.0622
## hk_12c~~hk_12c                   1.1824  0.1318 20000   0.9245   1.4409
## mvpaagt_24~~mvpaagt_24         254.6893 31.7855 20000 193.1809 317.5589
## groupfu~~groupfu                 0.2499  0.0229 20000   0.2053   0.2942
## hk_0c~~hk_0c                     2.0150  0.1840 20000   1.6525   2.3742
## mvpaagt_0c~~mvpaagt_0c         815.1445 75.5602 20000 665.9000 961.1303
## HIE~~HIE                         0.2231  0.0201 20000   0.1839   0.2631
## LIE~~LIE                         0.2245  0.0205 20000   0.1842   0.2642
## sex_0~~sex_0                     0.2340  0.0213 20000   0.1925   0.2757
## age_0c~~age_0c                  57.7334  5.2423 20000  47.4380  67.9635
## bmi_0c~~bmi_0c                  23.6379  2.1543 20000  19.3266  27.7932
## painvas_0c~~painvas_0c           3.9652  0.3645 20000   3.2433   4.6714
## poshee_0c~~poshee_0c             0.7573  0.0694 20000   0.6226   0.8947
## swesourceNA_0c~~swesourceNA_0c   1.3522  0.1267 20000   1.1046   1.5968
## fam3_0~~fam3_0                   0.1470  0.0134 20000   0.1204   0.1730
## hk_12c~1                        -0.4534  0.1887 20000  -0.8175  -0.0800
## mvpaagt_24~1                    36.6469  3.2663 20000  30.2387  43.0854
## groupfu~1                        0.5104  0.0322 20000   0.4469   0.5732
## hk_0c~1                          0.0000  0.0909 20000  -0.1767   0.1793
## mvpaagt_0c~1                    -0.3367  1.8776 20000  -3.9794   3.3655
## HIE~1                            0.3361  0.0301 20000   0.2775   0.3954
## LIE~1                            0.3402  0.0305 20000   0.2802   0.3999
## sex_0~1                          0.3734  0.0312 20000   0.3122   0.4342
## age_0c~1                         0.0000  0.4864 20000  -0.9590   0.9472
## bmi_0c~1                         0.0000  0.3130 20000  -0.6182   0.6124
## painvas_0c~1                    -0.0055  0.1297 20000  -0.2619   0.2480
## poshee_0c~1                      0.0012  0.0563 20000  -0.1109   0.1102
## swesourceNA_0c~1                 0.0047  0.0765 20000  -0.1440   0.1547
## fam3_0~1                         0.1789  0.0248 20000   0.1303   0.2277
## ind                              0.6718  0.6394 20000  -0.2523   2.1982
## total                            3.0830  2.9937 20000  -2.7931   8.9679

Action Control at 18 Months

model <- '
# Direct Effects
hk_18c ~ a1*groupfu + a2*hk_0c + a3*mvpaagt_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
mvpaagt_24 ~ c1*groupfu + c2*hk_0c + c3*mvpaagt_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*hk_18c

# Covariances
groupfu ~~ hk_0c + mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
hk_0c ~~ mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
mvpaagt_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 412 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        15
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   hk_18c ~                                                              
##     groupfu   (a1)    0.300    0.165    1.824    0.068   -0.022    0.623
##     hk_0c     (a2)    0.307    0.061    5.046    0.000    0.188    0.426
##     mvpgt_0c  (a3)    0.007    0.003    2.319    0.020    0.001    0.013
##     HIE       (a4)    0.402    0.201    2.003    0.045    0.009    0.795
##     LIE       (a5)    0.295    0.203    1.449    0.147   -0.104    0.693
##     sex_0     (a6)   -0.179    0.169   -1.062    0.288   -0.510    0.152
##     age_0c    (a7)    0.023    0.012    1.832    0.067   -0.002    0.047
##     bmi_0c    (a8)    0.022    0.018    1.225    0.220   -0.013    0.057
##     panvs_0c  (a9)    0.077    0.046    1.667    0.095   -0.013    0.167
##     poshe_0c (a10)    0.169    0.095    1.785    0.074   -0.017    0.354
##     swsrNA_0 (a11)   -0.177    0.084   -2.111    0.035   -0.341   -0.013
##     fam3_0   (a12)    0.027    0.212    0.125    0.900   -0.390    0.443
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    2.276    3.054    0.745    0.456   -3.710    8.263
##     hk_0c     (c2)    0.991    1.141    0.868    0.385   -1.246    3.228
##     mvpgt_0c  (c3)    0.704    0.058   12.093    0.000    0.590    0.819
##     HIE       (c4)   -3.898    3.566   -1.093    0.274  -10.887    3.091
##     LIE       (c5)    2.713    3.639    0.746    0.456   -4.419    9.846
##     sex_0     (c6)   -1.359    2.947   -0.461    0.645   -7.135    4.417
##     age_0c    (c7)   -0.386    0.234   -1.645    0.100   -0.845    0.074
##     bmi_0c    (c8)   -0.323    0.343   -0.943    0.346   -0.995    0.348
##     panvs_0c  (c9)   -2.114    0.838   -2.523    0.012   -3.757   -0.472
##     poshe_0c (c10)   -0.316    1.700   -0.186    0.853   -3.647    3.016
##     swsrNA_0 (c11)   -2.361    1.477   -1.599    0.110   -5.255    0.533
##     fam3_0   (c12)    4.831    3.818    1.265    0.206   -2.652   12.315
##     hk_18c    (b1)    2.015    1.652    1.220    0.223   -1.222    5.252
## 
## Covariances:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                             
##     hk_0c             -0.040    0.046   -0.874    0.382   -0.130    0.050
##     mvpaagt_0c        -0.750    0.936   -0.802    0.423   -2.584    1.084
##     HIE                0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE               -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0              0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c            -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c            -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c        -0.151    0.065   -2.312    0.021   -0.279   -0.023
##     poshee_0c          0.004    0.028    0.147    0.883   -0.051    0.059
##     swesourceNA_0c     0.070    0.038    1.807    0.071   -0.006    0.145
##     fam3_0            -0.033    0.013   -2.607    0.009   -0.057   -0.008
##   hk_0c ~~                                                               
##     mvpaagt_0c         2.417    2.677    0.903    0.367   -2.829    7.663
##     HIE                0.047    0.043    1.092    0.275   -0.038    0.132
##     LIE               -0.001    0.043   -0.013    0.989   -0.085    0.084
##     sex_0             -0.022    0.044   -0.490    0.624   -0.108    0.065
##     age_0c             2.159    0.709    3.047    0.002    0.770    3.548
##     bmi_0c            -0.668    0.447   -1.496    0.135   -1.544    0.207
##     painvas_0c        -0.068    0.184   -0.368    0.713   -0.428    0.293
##     poshee_0c          0.172    0.081    2.125    0.034    0.013    0.331
##     swesourceNA_0c    -0.142    0.109   -1.306    0.192   -0.356    0.071
##     fam3_0             0.003    0.035    0.074    0.941   -0.066    0.071
##   mvpaagt_0c ~~                                                          
##     HIE               -0.849    0.888   -0.957    0.339   -2.589    0.890
##     LIE                0.144    0.883    0.163    0.870   -1.587    1.875
##     sex_0              0.779    0.909    0.857    0.392   -1.003    2.560
##     age_0c           -51.388   14.500   -3.544    0.000  -79.807  -22.969
##     bmi_0c           -23.835    9.266   -2.572    0.010  -41.997   -5.673
##     painvas_0c         6.434    3.796    1.695    0.090   -1.007   13.874
##     poshee_0c          2.641    1.642    1.609    0.108   -0.577    5.859
##     swesourceNA_0c     1.543    2.208    0.699    0.484   -2.783    5.870
##     fam3_0             0.288    0.708    0.406    0.684   -1.101    1.676
##   HIE ~~                                                                 
##     LIE               -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0             -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c             0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c             0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c        -0.025    0.061   -0.411    0.681   -0.145    0.095
##     poshee_0c          0.045    0.027    1.697    0.090   -0.007    0.098
##     swesourceNA_0c    -0.035    0.036   -0.975    0.330   -0.107    0.036
##     fam3_0             0.002    0.012    0.180    0.857   -0.021    0.025
##   LIE ~~                                                                 
##     sex_0              0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c            -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c            -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c         0.037    0.062    0.600    0.549   -0.084    0.158
##     poshee_0c         -0.040    0.027   -1.512    0.130   -0.093    0.012
##     swesourceNA_0c     0.010    0.036    0.275    0.784   -0.061    0.081
##     fam3_0            -0.006    0.012   -0.546    0.585   -0.029    0.017
##   sex_0 ~~                                                               
##     age_0c             0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c            -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c        -0.008    0.063   -0.133    0.895   -0.131    0.114
##     poshee_0c         -0.034    0.027   -1.263    0.207   -0.088    0.019
##     swesourceNA_0c    -0.074    0.037   -1.998    0.046   -0.147   -0.001
##     fam3_0            -0.025    0.012   -2.098    0.036   -0.049   -0.002
##   age_0c ~~                                                              
##     bmi_0c            -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c        -2.728    0.995   -2.742    0.006   -4.678   -0.778
##     poshee_0c         -1.298    0.434   -2.987    0.003   -2.149   -0.446
##     swesourceNA_0c    -1.103    0.581   -1.899    0.058   -2.242    0.036
##     fam3_0            -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                              
##     painvas_0c         1.808    0.642    2.818    0.005    0.551    3.065
##     poshee_0c          0.084    0.274    0.308    0.758   -0.453    0.622
##     swesourceNA_0c     0.618    0.374    1.651    0.099   -0.116    1.352
##     fam3_0             0.055    0.121    0.460    0.645   -0.181    0.292
##   painvas_0c ~~                                                          
##     poshee_0c         -0.035    0.120   -0.288    0.773   -0.270    0.201
##     swesourceNA_0c     0.342    0.153    2.236    0.025    0.042    0.643
##     fam3_0             0.065    0.051    1.280    0.201   -0.035    0.164
##   poshee_0c ~~                                                           
##     swesourceNA_0c    -0.124    0.067   -1.861    0.063   -0.255    0.007
##     fam3_0             0.002    0.022    0.112    0.911   -0.040    0.045
##   swesourceNA_0c ~~                                                      
##     fam3_0             0.007    0.029    0.232    0.817   -0.051    0.064
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_18c           -0.352    0.175   -2.007    0.045   -0.696   -0.008
##    .mvpaagt_24       36.713    3.311   11.087    0.000   30.223   43.204
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     hk_0c             0.000    0.091    0.000    1.000   -0.179    0.179
##     mvpaagt_0c       -0.332    1.870   -0.178    0.859   -3.997    3.332
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c            0.000    0.489    0.000    1.000   -0.959    0.959
##     bmi_0c            0.000    0.313    0.000    1.000   -0.614    0.614
##     painvas_0c       -0.004    0.130   -0.035    0.972   -0.258    0.249
##     poshee_0c         0.002    0.056    0.044    0.965   -0.108    0.113
##     swesourceNA_0c    0.001    0.076    0.019    0.985   -0.148    0.151
##     fam3_0            0.179    0.025    7.230    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_18c            0.961    0.111    8.676    0.000    0.744    1.178
##    .mvpaagt_24      255.200   31.724    8.044    0.000  193.023  317.377
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     hk_0c             2.015    0.184   10.977    0.000    1.655    2.375
##     mvpaagt_0c      816.253   75.940   10.749    0.000  667.412  965.093
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.962    0.364   10.873    0.000    3.247    4.676
##     poshee_0c         0.758    0.069   10.940    0.000    0.622    0.894
##     swesourceNA_0c    1.349    0.126   10.747    0.000    1.103    1.595
##     fam3_0            0.147    0.013   10.956    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.605    0.608    0.996    0.319   -0.586    1.796
##     total             2.881    2.970    0.970    0.332   -2.940    8.703

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                     est      se     R     2.5%    97.5%
## a1                               0.3004  0.1657 20000  -0.0234   0.6205
## a2                               0.3070  0.0610 20000   0.1861   0.4254
## a3                               0.0071  0.0031 20000   0.0011   0.0131
## a4                               0.4019  0.2021 20000   0.0088   0.7976
## a5                               0.2948  0.2040 20000  -0.1031   0.6967
## a6                              -0.1794  0.1696 20000  -0.5132   0.1552
## a7                               0.0227  0.0125 20000  -0.0014   0.0472
## a8                               0.0217  0.0176 20000  -0.0130   0.0560
## a9                               0.0766  0.0463 20000  -0.0143   0.1671
## a10                              0.1689  0.0944 20000  -0.0154   0.3526
## a11                             -0.1769  0.0843 20000  -0.3391  -0.0110
## a12                              0.0266  0.2136 20000  -0.3932   0.4442
## c1                               2.2763  3.0708 20000  -3.6871   8.3699
## c2                               0.9912  1.1533 20000  -1.3008   3.2349
## c3                               0.7044  0.0579 20000   0.5904   0.8190
## c4                              -3.8978  3.5644 20000 -10.8808   3.1710
## c5                               2.7132  3.6547 20000  -4.3708   9.9328
## c6                              -1.3588  2.9116 20000  -7.1322   4.2730
## c7                              -0.3856  0.2338 20000  -0.8447   0.0752
## c8                              -0.3231  0.3430 20000  -0.9855   0.3490
## c9                              -2.1144  0.8426 20000  -3.7388  -0.4695
## c10                             -0.3155  1.6861 20000  -3.6212   2.9711
## c11                             -2.3609  1.4732 20000  -5.2431   0.5020
## c12                              4.8315  3.7939 20000  -2.5486  12.2719
## b1                               2.0147  1.6520 20000  -1.2298   5.2159
## groupfu~~hk_0c                  -0.0400  0.0459 20000  -0.1290   0.0509
## groupfu~~mvpaagt_0c             -0.7504  0.9369 20000  -2.5602   1.1017
## groupfu~~HIE                     0.0069  0.0152 20000  -0.0230   0.0371
## groupfu~~LIE                    -0.0035  0.0151 20000  -0.0327   0.0264
## groupfu~~sex_0                   0.0003  0.0156 20000  -0.0302   0.0307
## groupfu~~age_0c                 -0.0706  0.2434 20000  -0.5496   0.4079
## groupfu~~bmi_0c                 -0.0398  0.1569 20000  -0.3467   0.2663
## groupfu~~painvas_0c             -0.1512  0.0656 20000  -0.2806  -0.0238
## groupfu~~poshee_0c               0.0041  0.0281 20000  -0.0505   0.0597
## groupfu~~swesourceNA_0c          0.0695  0.0387 20000  -0.0060   0.1458
## groupfu~~fam3_0                 -0.0327  0.0126 20000  -0.0571  -0.0082
## hk_0c~~mvpaagt_0c                2.4168  2.6839 20000  -2.9755   7.5769
## hk_0c~~HIE                       0.0473  0.0434 20000  -0.0381   0.1331
## hk_0c~~LIE                      -0.0006  0.0432 20000  -0.0851   0.0838
## hk_0c~~sex_0                    -0.0217  0.0442 20000  -0.1086   0.0639
## hk_0c~~age_0c                    2.1591  0.7070 20000   0.7854   3.5514
## hk_0c~~bmi_0c                   -0.6683  0.4463 20000  -1.5556   0.2087
## hk_0c~~painvas_0c               -0.0677  0.1831 20000  -0.4242   0.2901
## hk_0c~~poshee_0c                 0.1720  0.0812 20000   0.0119   0.3307
## hk_0c~~swesourceNA_0c           -0.1425  0.1097 20000  -0.3574   0.0720
## hk_0c~~fam3_0                    0.0026  0.0350 20000  -0.0658   0.0712
## mvpaagt_0c~~HIE                 -0.8491  0.8908 20000  -2.6001   0.9058
## mvpaagt_0c~~LIE                  0.1444  0.8821 20000  -1.5569   1.8849
## mvpaagt_0c~~sex_0                0.7785  0.9053 20000  -0.9974   2.5469
## mvpaagt_0c~~age_0c             -51.3877 14.5146 20000 -79.9773 -23.0103
## mvpaagt_0c~~bmi_0c             -23.8351  9.2774 20000 -42.0984  -5.7410
## mvpaagt_0c~~painvas_0c           6.4338  3.7794 20000  -0.9088  13.9272
## mvpaagt_0c~~poshee_0c            2.6411  1.6544 20000  -0.6101   5.8721
## mvpaagt_0c~~swesourceNA_0c       1.5433  2.2148 20000  -2.7726   5.8516
## mvpaagt_0c~~fam3_0               0.2879  0.7064 20000  -1.0944   1.6789
## HIE~~LIE                        -0.1144  0.0161 20000  -0.1453  -0.0826
## HIE~~sex_0                      -0.0010  0.0147 20000  -0.0302   0.0274
## HIE~~age_0c                      0.1878  0.2338 20000  -0.2612   0.6491
## HIE~~bmi_0c                      0.3432  0.1504 20000   0.0478   0.6351
## HIE~~painvas_0c                 -0.0251  0.0612 20000  -0.1451   0.0955
## HIE~~poshee_0c                   0.0454  0.0267 20000  -0.0066   0.0980
## HIE~~swesourceNA_0c             -0.0354  0.0363 20000  -0.1065   0.0362
## HIE~~fam3_0                      0.0021  0.0117 20000  -0.0209   0.0250
## LIE~~sex_0                       0.0016  0.0150 20000  -0.0277   0.0308
## LIE~~age_0c                     -0.2255  0.2332 20000  -0.6830   0.2407
## LIE~~bmi_0c                     -0.3705  0.1504 20000  -0.6649  -0.0751
## LIE~~painvas_0c                  0.0369  0.0616 20000  -0.0839   0.1571
## LIE~~poshee_0c                  -0.0404  0.0268 20000  -0.0926   0.0123
## LIE~~swesourceNA_0c              0.0099  0.0360 20000  -0.0606   0.0811
## LIE~~fam3_0                     -0.0064  0.0118 20000  -0.0295   0.0169
## sex_0~~age_0c                    0.0533  0.2368 20000  -0.4112   0.5182
## sex_0~~bmi_0c                   -0.1011  0.1519 20000  -0.3972   0.1999
## sex_0~~painvas_0c               -0.0083  0.0629 20000  -0.1309   0.1148
## sex_0~~poshee_0c                -0.0344  0.0271 20000  -0.0878   0.0177
## sex_0~~swesourceNA_0c           -0.0743  0.0370 20000  -0.1460  -0.0015
## sex_0~~fam3_0                   -0.0253  0.0121 20000  -0.0494  -0.0015
## age_0c~~bmi_0c                  -1.6174  2.3946 20000  -6.3351   3.0672
## age_0c~~painvas_0c              -2.7282  0.9853 20000  -4.6647  -0.7996
## age_0c~~poshee_0c               -1.2976  0.4332 20000  -2.1529  -0.4581
## age_0c~~swesourceNA_0c          -1.1031  0.5783 20000  -2.2514   0.0287
## age_0c~~fam3_0                  -0.0283  0.1863 20000  -0.3955   0.3355
## bmi_0c~~painvas_0c               1.8081  0.6416 20000   0.5443   3.0636
## bmi_0c~~poshee_0c                0.0845  0.2744 20000  -0.4545   0.6256
## bmi_0c~~swesourceNA_0c           0.6180  0.3720 20000  -0.1201   1.3442
## bmi_0c~~fam3_0                   0.0555  0.1217 20000  -0.1813   0.2967
## painvas_0c~~poshee_0c           -0.0346  0.1203 20000  -0.2695   0.2013
## painvas_0c~~swesourceNA_0c       0.3424  0.1538 20000   0.0381   0.6423
## painvas_0c~~fam3_0               0.0649  0.0506 20000  -0.0333   0.1643
## poshee_0c~~swesourceNA_0c       -0.1242  0.0670 20000  -0.2546   0.0058
## poshee_0c~~fam3_0                0.0024  0.0215 20000  -0.0396   0.0454
## swesourceNA_0c~~fam3_0           0.0068  0.0292 20000  -0.0508   0.0642
## hk_18c~~hk_18c                   0.9606  0.1094 20000   0.7458   1.1755
## mvpaagt_24~~mvpaagt_24         255.1996 31.7837 20000 193.7564 318.0138
## groupfu~~groupfu                 0.2499  0.0230 20000   0.2056   0.2960
## hk_0c~~hk_0c                     2.0150  0.1849 20000   1.6561   2.3772
## mvpaagt_0c~~mvpaagt_0c         816.2528 75.6920 20000 669.6590 965.6656
## HIE~~HIE                         0.2231  0.0204 20000   0.1835   0.2634
## LIE~~LIE                         0.2245  0.0205 20000   0.1849   0.2648
## sex_0~~sex_0                     0.2340  0.0212 20000   0.1925   0.2758
## age_0c~~age_0c                  57.7334  5.2402 20000  47.4336  68.0973
## bmi_0c~~bmi_0c                  23.6379  2.1579 20000  19.5006  27.9156
## painvas_0c~~painvas_0c           3.9616  0.3644 20000   3.2339   4.6721
## poshee_0c~~poshee_0c             0.7584  0.0689 20000   0.6235   0.8925
## swesourceNA_0c~~swesourceNA_0c   1.3494  0.1258 20000   1.1002   1.5948
## fam3_0~~fam3_0                   0.1470  0.0134 20000   0.1211   0.1737
## hk_18c~1                        -0.3520  0.1766 20000  -0.7020  -0.0059
## mvpaagt_24~1                    36.7135  3.2875 20000  30.2339  43.2055
## groupfu~1                        0.5104  0.0323 20000   0.4465   0.5742
## hk_0c~1                          0.0000  0.0908 20000  -0.1773   0.1777
## mvpaagt_0c~1                    -0.3325  1.8782 20000  -4.0416   3.2951
## HIE~1                            0.3361  0.0304 20000   0.2767   0.3959
## LIE~1                            0.3402  0.0302 20000   0.2811   0.3992
## sex_0~1                          0.3734  0.0313 20000   0.3121   0.4348
## age_0c~1                         0.0000  0.4896 20000  -0.9738   0.9627
## bmi_0c~1                         0.0000  0.3124 20000  -0.6122   0.6053
## painvas_0c~1                    -0.0045  0.1292 20000  -0.2594   0.2496
## poshee_0c~1                      0.0025  0.0568 20000  -0.1076   0.1136
## swesourceNA_0c~1                 0.0015  0.0766 20000  -0.1506   0.1503
## fam3_0~1                         0.1789  0.0247 20000   0.1312   0.2278
## ind                              0.6051  0.6738 20000  -0.3683   2.2609
## total                            2.8815  3.0081 20000  -2.9904   8.8564

Collaborative Planning at 18 Months

model <- '
# Direct Effects
collimpint_18c ~ a1*groupfu + a2*collimpint_0c + a3*mvpaagt_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
mvpaagt_24 ~ c1*groupfu + c2*collimpint_0c + c3*mvpaagt_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*collimpint_18c

# Covariances
groupfu ~~ collimpint_0c + mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
collimpint_0c ~~ mvpaagt_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
mvpaagt_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 453 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        24
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   collimpint_18c ~                                                      
##     groupfu   (a1)    0.446    0.401    1.111    0.266   -0.341    1.233
##     cllmpn_0  (a2)    0.398    0.096    4.162    0.000    0.211    0.586
##     mvpgt_0c  (a3)    0.015    0.007    2.153    0.031    0.001    0.028
##     HIE       (a4)   -0.053    0.433   -0.123    0.902   -0.902    0.796
##     LIE       (a5)   -0.740    0.460   -1.610    0.108   -1.641    0.161
##     sex_0     (a6)   -0.245    0.387   -0.634    0.526   -1.003    0.513
##     age_0c    (a7)    0.064    0.030    2.155    0.031    0.006    0.122
##     bmi_0c    (a8)   -0.059    0.037   -1.585    0.113   -0.132    0.014
##     panvs_0c  (a9)   -0.031    0.110   -0.279    0.781   -0.247    0.186
##     poshe_0c (a10)    0.185    0.224    0.829    0.407   -0.253    0.624
##     swsrNA_0 (a11)   -0.196    0.216   -0.910    0.363   -0.619    0.227
##     fam3_0   (a12)   -0.149    0.541   -0.275    0.783   -1.209    0.911
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    2.530    3.101    0.816    0.415   -3.549    8.609
##     cllmpn_0  (c2)   -0.512    1.132   -0.453    0.651   -2.731    1.706
##     mvpgt_0c  (c3)    0.720    0.059   12.111    0.000    0.603    0.836
##     HIE       (c4)   -2.763    3.596   -0.768    0.442   -9.811    4.286
##     LIE       (c5)    4.572    3.890    1.175    0.240   -3.053   12.197
##     sex_0     (c6)   -1.366    3.005   -0.455    0.649   -7.255    4.523
##     age_0c    (c7)   -0.297    0.241   -1.233    0.217   -0.770    0.175
##     bmi_0c    (c8)   -0.149    0.367   -0.407    0.684   -0.869    0.570
##     panvs_0c  (c9)   -1.836    0.837   -2.193    0.028   -3.476   -0.195
##     poshe_0c (c10)    0.435    1.675    0.260    0.795   -2.848    3.718
##     swsrNA_0 (c11)   -2.618    1.497   -1.749    0.080   -5.552    0.316
##     fam3_0   (c12)    4.747    3.903    1.217    0.224   -2.901   12.396
##     cllmp_18  (b1)    1.098    1.476    0.743    0.457   -1.796    3.991
## 
## Covariances:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                             
##     collimpint_0c     -0.013    0.092   -0.136    0.892   -0.194    0.169
##     mvpaagt_0c        -0.757    0.936   -0.809    0.419   -2.591    1.077
##     HIE                0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE               -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0              0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c            -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c            -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c        -0.151    0.065   -2.309    0.021   -0.279   -0.023
##     poshee_0c          0.003    0.028    0.099    0.921   -0.052    0.058
##     swesourceNA_0c     0.072    0.039    1.867    0.062   -0.004    0.147
##     fam3_0            -0.033    0.013   -2.603    0.009   -0.057   -0.008
##   collimpint_0c ~~                                                       
##     mvpaagt_0c         5.902    5.095    1.158    0.247   -4.084   15.888
##     HIE                0.065    0.084    0.771    0.441   -0.100    0.229
##     LIE                0.005    0.091    0.056    0.955   -0.173    0.183
##     sex_0              0.054    0.090    0.602    0.547   -0.122    0.231
##     age_0c             0.169    1.422    0.119    0.906   -2.618    2.956
##     bmi_0c            -0.441    0.885   -0.499    0.618   -2.176    1.293
##     painvas_0c        -0.002    0.384   -0.005    0.996   -0.755    0.751
##     poshee_0c          0.216    0.160    1.351    0.177   -0.097    0.529
##     swesourceNA_0c    -0.388    0.235   -1.648    0.099   -0.849    0.073
##     fam3_0            -0.060    0.083   -0.720    0.472   -0.224    0.104
##   mvpaagt_0c ~~                                                          
##     HIE               -0.847    0.888   -0.954    0.340   -2.586    0.893
##     LIE                0.142    0.883    0.160    0.873   -1.590    1.873
##     sex_0              0.811    0.909    0.892    0.372   -0.971    2.592
##     age_0c           -51.589   14.507   -3.556    0.000  -80.022  -23.156
##     bmi_0c           -23.673    9.270   -2.554    0.011  -41.841   -5.504
##     painvas_0c         6.541    3.801    1.721    0.085   -0.909   13.991
##     poshee_0c          2.726    1.641    1.661    0.097   -0.491    5.943
##     swesourceNA_0c     1.511    2.211    0.683    0.494   -2.823    5.844
##     fam3_0             0.283    0.709    0.400    0.689   -1.106    1.672
##   HIE ~~                                                                 
##     LIE               -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0             -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c             0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c             0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c        -0.025    0.061   -0.411    0.681   -0.145    0.095
##     poshee_0c          0.044    0.027    1.631    0.103   -0.009    0.096
##     swesourceNA_0c    -0.035    0.036   -0.954    0.340   -0.106    0.037
##     fam3_0             0.002    0.012    0.177    0.860   -0.021    0.025
##   LIE ~~                                                                 
##     sex_0              0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c            -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c            -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c         0.039    0.062    0.626    0.531   -0.082    0.159
##     poshee_0c         -0.039    0.027   -1.479    0.139   -0.092    0.013
##     swesourceNA_0c     0.009    0.036    0.253    0.800   -0.062    0.080
##     fam3_0            -0.006    0.012   -0.540    0.589   -0.029    0.017
##   sex_0 ~~                                                               
##     age_0c             0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c            -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c        -0.009    0.063   -0.136    0.892   -0.131    0.114
##     poshee_0c         -0.033    0.027   -1.227    0.220   -0.087    0.020
##     swesourceNA_0c    -0.075    0.037   -2.023    0.043   -0.148   -0.002
##     fam3_0            -0.025    0.012   -2.101    0.036   -0.049   -0.002
##   age_0c ~~                                                              
##     bmi_0c            -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c        -2.731    0.996   -2.744    0.006   -4.683   -0.780
##     poshee_0c         -1.304    0.434   -3.004    0.003   -2.155   -0.453
##     swesourceNA_0c    -1.110    0.582   -1.908    0.056   -2.250    0.030
##     fam3_0            -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                              
##     painvas_0c         1.808    0.642    2.817    0.005    0.550    3.067
##     poshee_0c          0.062    0.274    0.225    0.822   -0.476    0.599
##     swesourceNA_0c     0.642    0.375    1.712    0.087   -0.093    1.377
##     fam3_0             0.056    0.121    0.466    0.641   -0.180    0.292
##   painvas_0c ~~                                                          
##     poshee_0c         -0.029    0.120   -0.241    0.809   -0.264    0.206
##     swesourceNA_0c     0.346    0.153    2.251    0.024    0.045    0.646
##     fam3_0             0.067    0.051    1.315    0.189   -0.033    0.167
##   poshee_0c ~~                                                           
##     swesourceNA_0c    -0.123    0.067   -1.840    0.066   -0.253    0.008
##     fam3_0             0.003    0.022    0.134    0.894   -0.039    0.045
##   swesourceNA_0c ~~                                                      
##     fam3_0             0.006    0.029    0.189    0.850   -0.052    0.063
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_18c    0.019    0.381    0.049    0.961   -0.729    0.766
##    .mvpaagt_24       35.820    3.338   10.730    0.000   29.277   42.363
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     collimpint_0c    -0.133    0.187   -0.709    0.478   -0.500    0.234
##     mvpaagt_0c       -0.310    1.870   -0.166    0.869   -3.976    3.356
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c            0.000    0.489    0.000    1.000   -0.959    0.959
##     bmi_0c            0.000    0.313    0.000    1.000   -0.614    0.614
##     painvas_0c       -0.004    0.130   -0.031    0.975   -0.258    0.250
##     poshee_0c        -0.000    0.056   -0.004    0.997   -0.110    0.110
##     swesourceNA_0c    0.004    0.077    0.047    0.963   -0.146    0.154
##     fam3_0            0.179    0.025    7.234    0.000    0.131    0.228
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_18c    2.450    0.406    6.040    0.000    1.655    3.245
##    .mvpaagt_24      259.217   32.831    7.895    0.000  194.870  323.565
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     collimpint_0c     4.384    0.550    7.969    0.000    3.306    5.462
##     mvpaagt_0c      816.862   76.022   10.745    0.000  667.862  965.861
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.965    0.365   10.864    0.000    3.250    4.681
##     poshee_0c         0.757    0.069   10.958    0.000    0.622    0.892
##     swesourceNA_0c    1.352    0.126   10.726    0.000    1.105    1.600
##     fam3_0            0.147    0.013   10.957    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.490    0.788    0.622    0.534   -1.054    2.033
##     total             3.020    3.012    1.003    0.316   -2.883    8.922

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                     est      se     R     2.5%    97.5%
## a1                               0.4461  0.4015 20000  -0.3424   1.2305
## a2                               0.3982  0.0962 20000   0.2110   0.5862
## a3                               0.0146  0.0068 20000   0.0013   0.0279
## a4                              -0.0531  0.4327 20000  -0.9033   0.7933
## a5                              -0.7401  0.4574 20000  -1.6450   0.1594
## a6                              -0.2451  0.3911 20000  -1.0132   0.5224
## a7                               0.0637  0.0295 20000   0.0062   0.1219
## a8                              -0.0591  0.0373 20000  -0.1318   0.0146
## a9                              -0.0308  0.1103 20000  -0.2460   0.1887
## a10                              0.1854  0.2238 20000  -0.2538   0.6257
## a11                             -0.1962  0.2164 20000  -0.6195   0.2281
## a12                             -0.1489  0.5379 20000  -1.2012   0.9127
## c1                               2.5301  3.0952 20000  -3.5096   8.5611
## c2                              -0.5124  1.1325 20000  -2.7271   1.7275
## c3                               0.7196  0.0594 20000   0.6024   0.8371
## c4                              -2.7626  3.6144 20000  -9.7746   4.4076
## c5                               4.5718  3.8737 20000  -3.0205  12.1935
## c6                              -1.3661  2.9997 20000  -7.3159   4.4930
## c7                              -0.2975  0.2413 20000  -0.7685   0.1728
## c8                              -0.1494  0.3681 20000  -0.8721   0.5685
## c9                              -1.8355  0.8377 20000  -3.4750  -0.2096
## c10                              0.4351  1.6668 20000  -2.8649   3.6980
## c11                             -2.6184  1.4999 20000  -5.5594   0.3104
## c12                              4.7475  3.8771 20000  -2.7206  12.4518
## b1                               1.0976  1.4712 20000  -1.8024   3.9743
## groupfu~~collimpint_0c          -0.0126  0.0927 20000  -0.1957   0.1703
## groupfu~~mvpaagt_0c             -0.7571  0.9382 20000  -2.6029   1.0781
## groupfu~~HIE                     0.0069  0.0152 20000  -0.0229   0.0368
## groupfu~~LIE                    -0.0035  0.0153 20000  -0.0333   0.0268
## groupfu~~sex_0                   0.0003  0.0157 20000  -0.0304   0.0315
## groupfu~~age_0c                 -0.0706  0.2468 20000  -0.5548   0.4139
## groupfu~~bmi_0c                 -0.0398  0.1576 20000  -0.3502   0.2704
## groupfu~~painvas_0c             -0.1512  0.0654 20000  -0.2789  -0.0229
## groupfu~~poshee_0c               0.0028  0.0279 20000  -0.0522   0.0569
## groupfu~~swesourceNA_0c          0.0719  0.0384 20000  -0.0033   0.1473
## groupfu~~fam3_0                 -0.0327  0.0126 20000  -0.0575  -0.0081
## collimpint_0c~~mvpaagt_0c        5.9020  5.0774 20000  -4.0260  15.7658
## collimpint_0c~~HIE               0.0646  0.0837 20000  -0.1007   0.2298
## collimpint_0c~~LIE               0.0051  0.0911 20000  -0.1748   0.1840
## collimpint_0c~~sex_0             0.0542  0.0895 20000  -0.1211   0.2269
## collimpint_0c~~age_0c            0.1686  1.4296 20000  -2.5960   2.9684
## collimpint_0c~~bmi_0c           -0.4413  0.8917 20000  -2.2089   1.2862
## collimpint_0c~~painvas_0c       -0.0018  0.3793 20000  -0.7507   0.7326
## collimpint_0c~~poshee_0c         0.2160  0.1585 20000  -0.0928   0.5281
## collimpint_0c~~swesourceNA_0c   -0.3877  0.2379 20000  -0.8505   0.0782
## collimpint_0c~~fam3_0           -0.0601  0.0842 20000  -0.2252   0.1056
## mvpaagt_0c~~HIE                 -0.8467  0.8878 20000  -2.6140   0.8985
## mvpaagt_0c~~LIE                  0.1417  0.8842 20000  -1.6029   1.8961
## mvpaagt_0c~~sex_0                0.8106  0.9098 20000  -0.9847   2.6131
## mvpaagt_0c~~age_0c             -51.5894 14.5347 20000 -80.2785 -23.2978
## mvpaagt_0c~~bmi_0c             -23.6729  9.2792 20000 -42.0220  -5.5841
## mvpaagt_0c~~painvas_0c           6.5411  3.8327 20000  -1.0746  14.0629
## mvpaagt_0c~~poshee_0c            2.7258  1.6499 20000  -0.5278   5.9421
## mvpaagt_0c~~swesourceNA_0c       1.5108  2.2188 20000  -2.8077   5.8911
## mvpaagt_0c~~fam3_0               0.2832  0.7049 20000  -1.1130   1.6746
## HIE~~LIE                        -0.1144  0.0161 20000  -0.1459  -0.0824
## HIE~~sex_0                      -0.0010  0.0147 20000  -0.0300   0.0276
## HIE~~age_0c                      0.1878  0.2312 20000  -0.2630   0.6459
## HIE~~bmi_0c                      0.3432  0.1495 20000   0.0515   0.6343
## HIE~~painvas_0c                 -0.0251  0.0605 20000  -0.1434   0.0921
## HIE~~poshee_0c                   0.0436  0.0266 20000  -0.0079   0.0961
## HIE~~swesourceNA_0c             -0.0347  0.0365 20000  -0.1055   0.0368
## HIE~~fam3_0                      0.0021  0.0117 20000  -0.0211   0.0248
## LIE~~sex_0                       0.0016  0.0148 20000  -0.0271   0.0305
## LIE~~age_0c                     -0.2255  0.2311 20000  -0.6772   0.2228
## LIE~~bmi_0c                     -0.3705  0.1511 20000  -0.6666  -0.0736
## LIE~~painvas_0c                  0.0386  0.0607 20000  -0.0795   0.1563
## LIE~~poshee_0c                  -0.0395  0.0267 20000  -0.0919   0.0125
## LIE~~swesourceNA_0c              0.0092  0.0362 20000  -0.0622   0.0805
## LIE~~fam3_0                     -0.0064  0.0118 20000  -0.0295   0.0170
## sex_0~~age_0c                    0.0533  0.2371 20000  -0.4169   0.5179
## sex_0~~bmi_0c                   -0.1011  0.1529 20000  -0.4029   0.1967
## sex_0~~painvas_0c               -0.0085  0.0626 20000  -0.1330   0.1127
## sex_0~~poshee_0c                -0.0334  0.0274 20000  -0.0868   0.0203
## sex_0~~swesourceNA_0c           -0.0753  0.0372 20000  -0.1476  -0.0022
## sex_0~~fam3_0                   -0.0254  0.0122 20000  -0.0490  -0.0017
## age_0c~~bmi_0c                  -1.6174  2.4028 20000  -6.3535   3.0978
## age_0c~~painvas_0c              -2.7313  0.9912 20000  -4.6861  -0.7924
## age_0c~~poshee_0c               -1.3042  0.4357 20000  -2.1503  -0.4324
## age_0c~~swesourceNA_0c          -1.1096  0.5787 20000  -2.2345   0.0323
## age_0c~~fam3_0                  -0.0283  0.1868 20000  -0.3968   0.3377
## bmi_0c~~painvas_0c               1.8085  0.6427 20000   0.5532   3.0561
## bmi_0c~~poshee_0c                0.0616  0.2743 20000  -0.4795   0.6004
## bmi_0c~~swesourceNA_0c           0.6420  0.3740 20000  -0.1056   1.3659
## bmi_0c~~fam3_0                   0.0561  0.1210 20000  -0.1842   0.2935
## painvas_0c~~poshee_0c           -0.0290  0.1198 20000  -0.2617   0.2042
## painvas_0c~~swesourceNA_0c       0.3455  0.1540 20000   0.0451   0.6510
## painvas_0c~~fam3_0               0.0668  0.0511 20000  -0.0343   0.1665
## poshee_0c~~swesourceNA_0c       -0.1228  0.0672 20000  -0.2558   0.0083
## poshee_0c~~fam3_0                0.0029  0.0216 20000  -0.0394   0.0451
## swesourceNA_0c~~fam3_0           0.0055  0.0296 20000  -0.0519   0.0631
## collimpint_18c~~collimpint_18c   2.4504  0.4049 20000   1.6669   3.2365
## mvpaagt_24~~mvpaagt_24         259.2175 32.8924 20000 194.2269 322.8614
## groupfu~~groupfu                 0.2499  0.0227 20000   0.2053   0.2942
## collimpint_0c~~collimpint_0c     4.3841  0.5466 20000   3.3195   5.4671
## mvpaagt_0c~~mvpaagt_0c         816.8616 75.7720 20000 670.1354 966.2932
## HIE~~HIE                         0.2231  0.0203 20000   0.1830   0.2624
## LIE~~LIE                         0.2245  0.0204 20000   0.1845   0.2645
## sex_0~~sex_0                     0.2340  0.0213 20000   0.1921   0.2753
## age_0c~~age_0c                  57.7334  5.1820 20000  47.6135  67.8224
## bmi_0c~~bmi_0c                  23.6379  2.1644 20000  19.3453  27.8063
## painvas_0c~~painvas_0c           3.9652  0.3673 20000   3.2495   4.6886
## poshee_0c~~poshee_0c             0.7570  0.0692 20000   0.6210   0.8934
## swesourceNA_0c~~swesourceNA_0c   1.3524  0.1252 20000   1.1062   1.5967
## fam3_0~~fam3_0                   0.1470  0.0134 20000   0.1209   0.1737
## collimpint_18c~1                 0.0188  0.3833 20000  -0.7271   0.7648
## mvpaagt_24~1                    35.8198  3.3197 20000  29.2344  42.3475
## groupfu~1                        0.5104  0.0322 20000   0.4473   0.5727
## collimpint_0c~1                 -0.1328  0.1874 20000  -0.5010   0.2344
## mvpaagt_0c~1                    -0.3097  1.8786 20000  -3.9442   3.3987
## HIE~1                            0.3361  0.0301 20000   0.2781   0.3955
## LIE~1                            0.3402  0.0308 20000   0.2798   0.3998
## sex_0~1                          0.3734  0.0310 20000   0.3122   0.4339
## age_0c~1                         0.0000  0.4889 20000  -0.9757   0.9428
## bmi_0c~1                         0.0000  0.3148 20000  -0.6128   0.6148
## painvas_0c~1                    -0.0041  0.1293 20000  -0.2597   0.2486
## poshee_0c~1                     -0.0002  0.0563 20000  -0.1097   0.1127
## swesourceNA_0c~1                 0.0036  0.0769 20000  -0.1468   0.1524
## fam3_0~1                         0.1790  0.0246 20000   0.1308   0.2274
## ind                              0.4897  0.9796 20000  -1.1692   2.8548
## total                            3.0197  3.0707 20000  -2.9623   9.0491

Supplemental Material

S-Table 4

table <- CreateTableOne(data = data, 
               vars = c("age_0", # age
                        "sex_0", # sex
                        "fam1_0", # married
                        "fam2_0", # committed relationship
                        "fam3_0", # divorced
                        "fam4_0", # single
                        "fam5_0", # widowed
                        "highschl_0", # high school diploma
                        "univdgr_0", # university degree
                        "erwerb_0", # employed
                        "income_0", # income
                        "kids1_0", # children
                        "bmi_0", # body mass index
                        "klgrade_0", # Kellgren-Lawrence grade
                        "gonarthroseyears_0", # disease duration (knee osteoarthritis)
                        "comorbdich_0"), # comorbidity
               factorVars = c("sex_0", 
                              "fam1_0", 
                              "fam2_0", 
                              "fam3_0", 
                              "fam4_0", 
                              "fam5_0",
                              "highschl_0", 
                              "univdgr_0", 
                              "erwerb_0", 
                              "income_0", 
                              "kids1_0", 
                              "klgrade_0", 
                              "comorbdich_0"), 
               strata = "groupfu")

print(table, showAllLevels = TRUE)

##                                 Stratified by groupfu
##                                  level 0             1             p      test
##   n                                      118           123                    
##   age_0 (mean (SD))                    65.75 (7.28)  65.46 (7.95)   0.774     
##   sex_0 (%)                      0        74 (62.7)     77 (62.6)   1.000     
##                                  1        44 (37.3)     46 (37.4)             
##   fam1_0 (%)                     0        61 (51.7)     49 (40.2)   0.096     
##                                  1        57 (48.3)     73 (59.8)             
##   fam2_0 (%)                     0       104 (88.1)    107 (87.7)   1.000     
##                                  1        14 (11.9)     15 (12.3)             
##   fam3_0 (%)                     0        89 (75.4)    108 (88.5)   0.013     
##                                  1        29 (24.6)     14 (11.5)             
##   fam4_0 (%)                     0       101 (86.3)    111 (91.0)   0.351     
##                                  1        16 (13.7)     11 ( 9.0)             
##   fam5_0 (%)                     0       111 (94.9)    108 (88.5)   0.124     
##                                  1         6 ( 5.1)     14 (11.5)             
##   highschl_0 (%)                 0        53 (46.1)     50 (40.7)   0.475     
##                                  1        62 (53.9)     73 (59.3)             
##   univdgr_0 (%)                  0        68 (57.6)     64 (52.5)   0.500     
##                                  1        50 (42.4)     58 (47.5)             
##   erwerb_0 (%)                   0        78 (66.1)     76 (61.8)   0.574     
##                                  1        40 (33.9)     47 (38.2)             
##   income_0 (%)                   1         7 ( 6.2)     10 ( 8.6)   0.701     
##                                  2        25 (22.1)     20 (17.2)             
##                                  3        34 (30.1)     33 (28.4)             
##                                  4        47 (41.6)     53 (45.7)             
##   kids1_0 (%)                    0        31 (26.5)     21 (17.2)   0.114     
##                                  1        86 (73.5)    101 (82.8)             
##   bmi_0 (mean (SD))                    28.61 (5.45)  28.45 (4.27)   0.800     
##   klgrade_0 (%)                  2        41 (34.7)     44 (35.8)   0.975     
##                                  3        77 (65.3)     79 (64.2)             
##   gonarthroseyears_0 (mean (SD))       11.64 (9.92)  11.41 (10.52)  0.863     
##   comorbdich_0 (%)               0        16 (13.7)     23 (19.0)   0.349     
##                                  1       101 (86.3)     98 (81.0)

summary(table)

## 
##      ### Summary of continuous variables ###
## 
## groupfu: 0
##                      n miss p.miss mean sd median p25 p75  min max skew kurt
## age_0              118    0      0   66  7     67  62  70 45.0  80 -0.7  0.4
## bmi_0              118    0      0   29  5     27  25  32 19.2  46  0.9  0.2
## gonarthroseyears_0 118    3      3   12 10      8   4  17  0.4  39  1.0  0.2
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##                      n miss p.miss mean sd median p25 p75  min max skew kurt
## age_0              123    0      0   65  8     68  62  70 44.0  79 -0.8  0.3
## bmi_0              123    0      0   28  4     28  25  32 21.2  39  0.4 -0.8
## gonarthroseyears_0 123    4      3   11 11      8   4  17  0.2  49  1.5  2.0
## 
## p-values
##                      pNormal pNonNormal
## age_0              0.7741893  0.9483306
## bmi_0              0.8003143  0.6633502
## gonarthroseyears_0 0.8632258  0.7521321
## 
## Standardize mean differences
##                        1 vs 2
## age_0              0.03704565
## bmi_0              0.03254798
## gonarthroseyears_0 0.02256279
## 
## =======================================================================================
## 
##      ### Summary of categorical variables ### 
## 
## groupfu: 0
##           var   n miss p.miss level freq percent cum.percent
##         sex_0 118    0    0.0     0   74    62.7        62.7
##                                   1   44    37.3       100.0
##                                                             
##        fam1_0 118    0    0.0     0   61    51.7        51.7
##                                   1   57    48.3       100.0
##                                                             
##        fam2_0 118    0    0.0     0  104    88.1        88.1
##                                   1   14    11.9       100.0
##                                                             
##        fam3_0 118    0    0.0     0   89    75.4        75.4
##                                   1   29    24.6       100.0
##                                                             
##        fam4_0 118    1    0.8     0  101    86.3        86.3
##                                   1   16    13.7       100.0
##                                                             
##        fam5_0 118    1    0.8     0  111    94.9        94.9
##                                   1    6     5.1       100.0
##                                                             
##    highschl_0 118    3    2.5     0   53    46.1        46.1
##                                   1   62    53.9       100.0
##                                                             
##     univdgr_0 118    0    0.0     0   68    57.6        57.6
##                                   1   50    42.4       100.0
##                                                             
##      erwerb_0 118    0    0.0     0   78    66.1        66.1
##                                   1   40    33.9       100.0
##                                                             
##      income_0 118    5    4.2     1    7     6.2         6.2
##                                   2   25    22.1        28.3
##                                   3   34    30.1        58.4
##                                   4   47    41.6       100.0
##                                                             
##       kids1_0 118    1    0.8     0   31    26.5        26.5
##                                   1   86    73.5       100.0
##                                                             
##     klgrade_0 118    0    0.0     2   41    34.7        34.7
##                                   3   77    65.3       100.0
##                                                             
##  comorbdich_0 118    1    0.8     0   16    13.7        13.7
##                                   1  101    86.3       100.0
##                                                             
## --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 
## groupfu: 1
##           var   n miss p.miss level freq percent cum.percent
##         sex_0 123    0    0.0     0   77    62.6        62.6
##                                   1   46    37.4       100.0
##                                                             
##        fam1_0 123    1    0.8     0   49    40.2        40.2
##                                   1   73    59.8       100.0
##                                                             
##        fam2_0 123    1    0.8     0  107    87.7        87.7
##                                   1   15    12.3       100.0
##                                                             
##        fam3_0 123    1    0.8     0  108    88.5        88.5
##                                   1   14    11.5       100.0
##                                                             
##        fam4_0 123    1    0.8     0  111    91.0        91.0
##                                   1   11     9.0       100.0
##                                                             
##        fam5_0 123    1    0.8     0  108    88.5        88.5
##                                   1   14    11.5       100.0
##                                                             
##    highschl_0 123    0    0.0     0   50    40.7        40.7
##                                   1   73    59.3       100.0
##                                                             
##     univdgr_0 123    1    0.8     0   64    52.5        52.5
##                                   1   58    47.5       100.0
##                                                             
##      erwerb_0 123    0    0.0     0   76    61.8        61.8
##                                   1   47    38.2       100.0
##                                                             
##      income_0 123    7    5.7     1   10     8.6         8.6
##                                   2   20    17.2        25.9
##                                   3   33    28.4        54.3
##                                   4   53    45.7       100.0
##                                                             
##       kids1_0 123    1    0.8     0   21    17.2        17.2
##                                   1  101    82.8       100.0
##                                                             
##     klgrade_0 123    0    0.0     2   44    35.8        35.8
##                                   3   79    64.2       100.0
##                                                             
##  comorbdich_0 123    2    1.6     0   23    19.0        19.0
##                                   1   98    81.0       100.0
##                                                             
## 
## p-values
##                 pApprox     pExact
## sex_0        1.00000000 1.00000000
## fam1_0       0.09635448 0.09197240
## fam2_0       1.00000000 1.00000000
## fam3_0       0.01323183 0.01106838
## fam4_0       0.35083172 0.30864446
## fam5_0       0.12410799 0.10162519
## highschl_0   0.47460007 0.43340425
## univdgr_0    0.49980741 0.43875279
## erwerb_0     0.57360457 0.50501257
## income_0     0.70065819 0.71072304
## kids1_0      0.11367921 0.08712897
## klgrade_0    0.97455781 0.89335723
## comorbdich_0 0.34924513 0.29651910
## 
## Standardize mean differences
##                   1 vs 2
## sex_0        0.002278994
## fam1_0       0.232951982
## fam2_0       0.013215573
## fam3_0       0.345869681
## fam4_0       0.147293392
## fam5_0       0.231583652
## highschl_0   0.109865471
## univdgr_0    0.104032296
## erwerb_0     0.089916635
## income_0     0.158040040
## kids1_0      0.226050121
## klgrade_0    0.021488166
## comorbdich_0 0.144612053

S-Table 7

Action Planning at 12 Months

model <- '
# Direct Effects
acplan_12c ~ a1*groupfu + a2*acplan_0c + a3*mvpaagt_0c
mvpaagt_24 ~ c1*groupfu + c2*acplan_0c + c3*mvpaagt_0c + b1*acplan_12c

# Covariances
groupfu ~~ acplan_0c + mvpaagt_0c 
acplan_0c ~~ mvpaagt_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 83 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         7
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   acplan_12c ~                                                          
##     groupfu   (a1)    0.532    0.245    2.176    0.030    0.053    1.012
##     acplan_0c (a2)    0.381    0.070    5.446    0.000    0.244    0.518
##     mvpagt_0c (a3)    0.001    0.005    0.300    0.764   -0.008    0.010
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    2.308    3.109    0.743    0.458   -3.784    8.401
##     acplan_0c (c2)   -0.888    0.965   -0.921    0.357   -2.779    1.003
##     mvpagt_0c (c3)    0.728    0.057   12.691    0.000    0.616    0.840
##     acpln_12c (b1)    2.095    1.027    2.040    0.041    0.083    4.106
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     acplan_0c        -0.007    0.057   -0.132    0.895   -0.119    0.104
##     mvpaagt_0c       -0.570    0.933   -0.611    0.541   -2.399    1.258
##   acplan_0c ~~                                                          
##     mvpaagt_0c       15.802    3.431    4.606    0.000    9.078   22.526
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_12c       -0.225    0.167   -1.347    0.178   -0.553    0.102
##    .mvpaagt_24       36.978    2.160   17.117    0.000   32.744   41.212
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     acplan_0c        -0.000    0.113   -0.000    1.000   -0.222    0.222
##     mvpaagt_0c       -0.101    1.865   -0.054    0.957   -3.756    3.555
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_12c        2.331    0.261    8.921    0.000    1.819    2.844
##    .mvpaagt_24      291.355   36.507    7.981    0.000  219.804  362.907
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     acplan_0c         3.099    0.282   10.977    0.000    2.545    3.652
##     mvpaagt_0c      812.719   75.412   10.777    0.000  664.915  960.523
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               1.115    0.748    1.490    0.136   -0.351    2.582
##     total             3.423    3.086    1.109    0.267   -2.624    9.471

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                             est      se     R     2.5%    97.5%
## a1                       0.5324  0.2421 20000   0.0654   1.0124
## a2                       0.3806  0.0703 20000   0.2408   0.5167
## a3                       0.0014  0.0047 20000  -0.0078   0.0106
## c1                       2.3082  3.1032 20000  -3.7408   8.4148
## c2                      -0.8883  0.9649 20000  -2.7944   0.9780
## c3                       0.7280  0.0570 20000   0.6162   0.8413
## b1                       2.0946  1.0219 20000   0.1221   4.1035
## groupfu~~acplan_0c      -0.0075  0.0569 20000  -0.1199   0.1036
## groupfu~~mvpaagt_0c     -0.5705  0.9284 20000  -2.3597   1.2846
## acplan_0c~~mvpaagt_0c   15.8023  3.4247 20000   9.1155  22.4806
## acplan_12c~~acplan_12c   2.3314  0.2620 20000   1.8197   2.8626
## mvpaagt_24~~mvpaagt_24 291.3551 36.5764 20000 220.7085 363.6259
## groupfu~~groupfu         0.2499  0.0228 20000   0.2056   0.2942
## acplan_0c~~acplan_0c     3.0985  0.2830 20000   2.5403   3.6524
## mvpaagt_0c~~mvpaagt_0c 812.7194 75.1701 20000 667.3349 960.8293
## acplan_12c~1            -0.2254  0.1667 20000  -0.5534   0.0995
## mvpaagt_24~1            36.9782  2.1589 20000  32.7230  41.2474
## groupfu~1                0.5104  0.0323 20000   0.4474   0.5749
## acplan_0c~1              0.0000  0.1144 20000  -0.2244   0.2242
## mvpaagt_0c~1            -0.1007  1.8612 20000  -3.7151   3.5995
## ind                      1.1152  0.7816 20000  -0.0506   2.9488
## total                    3.4234  3.0902 20000  -2.6142   9.4886

Action Planning at 18 Months

model <- '
# Direct Effects
acplan_18c ~ a1*groupfu + a2*acplan_0c + a3*mvpaagt_0c
mvpaagt_24 ~ c1*groupfu + c2*acplan_0c + c3*mvpaagt_0c + b1*acplan_18c

# Covariances
groupfu ~~ acplan_0c + mvpaagt_0c 
acplan_0c ~~ mvpaagt_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 86 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         6
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   acplan_18c ~                                                          
##     groupfu   (a1)    0.413    0.257    1.605    0.108   -0.091    0.917
##     acplan_0c (a2)    0.181    0.076    2.379    0.017    0.032    0.331
##     mvpagt_0c (a3)    0.006    0.005    1.323    0.186   -0.003    0.016
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    3.044    3.165    0.962    0.336   -3.159    9.246
##     acplan_0c (c2)   -0.356    0.956   -0.372    0.710   -2.230    1.518
##     mvpagt_0c (c3)    0.730    0.058   12.527    0.000    0.616    0.844
##     acpln_18c (b1)    0.752    1.077    0.698    0.485   -1.359    2.863
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     acplan_0c        -0.007    0.057   -0.132    0.895   -0.119    0.104
##     mvpaagt_0c       -0.582    0.933   -0.623    0.533   -2.411    1.248
##   acplan_0c ~~                                                          
##     mvpaagt_0c       15.787    3.431    4.601    0.000    9.062   22.511
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_18c       -0.194    0.176   -1.101    0.271   -0.539    0.151
##    .mvpaagt_24       36.915    2.208   16.722    0.000   32.588   41.241
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     acplan_0c         0.000    0.113    0.000    1.000   -0.222    0.222
##     mvpaagt_0c       -0.115    1.865   -0.062    0.951   -3.771    3.541
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_18c        2.471    0.284    8.689    0.000    1.913    3.028
##    .mvpaagt_24      299.616   37.509    7.988    0.000  226.100  373.133
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     acplan_0c         3.099    0.282   10.977    0.000    2.545    3.652
##     mvpaagt_0c      812.905   75.433   10.776    0.000  665.058  960.752
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.311    0.490    0.634    0.526   -0.650    1.271
##     total             3.354    3.102    1.081    0.280   -2.726    9.435

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                             est      se     R     2.5%    97.5%
## a1                       0.4131  0.2550 20000  -0.0803   0.9196
## a2                       0.1814  0.0766 20000   0.0336   0.3329
## a3                       0.0063  0.0048 20000  -0.0032   0.0158
## c1                       3.0436  3.1597 20000  -3.1418   9.3081
## c2                      -0.3559  0.9571 20000  -2.2352   1.5031
## c3                       0.7301  0.0586 20000   0.6154   0.8445
## b1                       0.7520  1.0698 20000  -1.3540   2.8177
## groupfu~~acplan_0c      -0.0075  0.0563 20000  -0.1183   0.1016
## groupfu~~mvpaagt_0c     -0.5816  0.9273 20000  -2.4342   1.2010
## acplan_0c~~mvpaagt_0c   15.7867  3.4251 20000   9.0975  22.4711
## acplan_18c~~acplan_18c   2.4708  0.2850 20000   1.8957   3.0262
## mvpaagt_24~~mvpaagt_24 299.6164 37.5810 20000 227.0142 373.8996
## groupfu~~groupfu         0.2499  0.0229 20000   0.2056   0.2943
## acplan_0c~~acplan_0c     3.0985  0.2830 20000   2.5398   3.6519
## mvpaagt_0c~~mvpaagt_0c 812.9048 75.1918 20000 667.5268 961.0821
## acplan_18c~1            -0.1937  0.1749 20000  -0.5371   0.1453
## mvpaagt_24~1            36.9146  2.2068 20000  32.5564  41.2440
## groupfu~1                0.5104  0.0323 20000   0.4473   0.5749
## acplan_0c~1              0.0000  0.1137 20000  -0.2229   0.2234
## mvpaagt_0c~1            -0.1152  1.8616 20000  -3.7291   3.5880
## ind                      0.3106  0.5582 20000  -0.6305   1.6545
## total                    3.3542  3.1074 20000  -2.7348   9.4903

Coping Planning at 12 Months

model <- '
# Direct Effects
coplan_12c ~ a1*groupfu + a2*coplan_0c + a3*mvpaagt_0c
mvpaagt_24 ~ c1*groupfu + c2*coplan_0c + c3*mvpaagt_0c + b1*coplan_12c

# Covariances
groupfu ~~ coplan_0c + mvpaagt_0c 
coplan_0c ~~ mvpaagt_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 81 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         8
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   coplan_12c ~                                                          
##     groupfu   (a1)    0.133    0.215    0.620    0.535   -0.289    0.555
##     coplan_0c (a2)    0.400    0.069    5.839    0.000    0.266    0.535
##     mvpagt_0c (a3)    0.005    0.004    1.358    0.175   -0.002    0.013
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    3.731    3.059    1.220    0.223   -2.265    9.726
##     coplan_0c (c2)    0.463    1.087    0.426    0.670   -1.666    2.593
##     mvpagt_0c (c3)    0.718    0.056   12.856    0.000    0.608    0.827
##     copln_12c (b1)    1.239    1.185    1.046    0.296   -1.084    3.563
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     coplan_0c        -0.019    0.051   -0.381    0.703   -0.119    0.080
##     mvpaagt_0c       -0.645    0.936   -0.689    0.491   -2.480    1.190
##   coplan_0c ~~                                                          
##     mvpaagt_0c        5.301    2.976    1.781    0.075   -0.532   11.134
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_12c       -0.058    0.147   -0.391    0.696   -0.346    0.231
##    .mvpaagt_24       36.478    2.173   16.791    0.000   32.220   40.736
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     coplan_0c         0.001    0.102    0.012    0.990   -0.198    0.201
##     mvpaagt_0c       -0.169    1.871   -0.090    0.928   -3.836    3.498
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_12c        1.821    0.203    8.964    0.000    1.423    2.219
##    .mvpaagt_24      294.419   36.766    8.008    0.000  222.360  366.479
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     coplan_0c         2.484    0.227   10.961    0.000    2.039    2.928
##     mvpaagt_0c      816.038   75.894   10.752    0.000  667.289  964.787
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.165    0.310    0.534    0.594   -0.442    0.773
##     total             3.896    3.077    1.266    0.205   -2.134    9.927

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                             est      se     R     2.5%    97.5%
## a1                       0.1334  0.2130 20000  -0.2777   0.5552
## a2                       0.4003  0.0689 20000   0.2639   0.5336
## a3                       0.0053  0.0040 20000  -0.0024   0.0131
## c1                       3.7309  3.0493 20000  -2.2163   9.7337
## c2                       0.4635  1.0877 20000  -1.6879   2.5983
## c3                       0.7176  0.0560 20000   0.6068   0.8268
## b1                       1.2394  1.1811 20000  -1.0621   3.5566
## groupfu~~coplan_0c      -0.0194  0.0506 20000  -0.1178   0.0800
## groupfu~~mvpaagt_0c     -0.6451  0.9304 20000  -2.4975   1.1438
## coplan_0c~~mvpaagt_0c    5.3011  2.9813 20000  -0.4693  11.1802
## coplan_12c~~coplan_12c   1.8213  0.2048 20000   1.4202   2.2223
## mvpaagt_24~~mvpaagt_24 294.4193 36.8362 20000 223.2514 367.1974
## groupfu~~groupfu         0.2499  0.0229 20000   0.2058   0.2944
## coplan_0c~~coplan_0c     2.4835  0.2271 20000   2.0438   2.9417
## mvpaagt_0c~~mvpaagt_0c 816.0379 75.6504 20000 669.7219 965.0971
## coplan_12c~1            -0.0576  0.1466 20000  -0.3459   0.2281
## mvpaagt_24~1            36.4783  2.1753 20000  32.2139  40.7795
## groupfu~1                0.5104  0.0323 20000   0.4476   0.5748
## coplan_0c~1              0.0012  0.1024 20000  -0.2006   0.2019
## mvpaagt_0c~1            -0.1692  1.8671 20000  -3.7956   3.5431
## ind                      0.1654  0.3997 20000  -0.5204   1.1274
## total                    3.8963  3.0791 20000  -2.1019   9.9700

Coping Planning at 18 Months

model <- '
# Direct Effects
coplan_18c ~ a1*groupfu + a2*coplan_0c + a3*mvpaagt_0c
mvpaagt_24 ~ c1*groupfu + c2*coplan_0c + c3*mvpaagt_0c + b1*coplan_18c

# Covariances
groupfu ~~ coplan_0c + mvpaagt_0c 
coplan_0c ~~ mvpaagt_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 83 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         7
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   coplan_18c ~                                                          
##     groupfu   (a1)    0.226    0.228    0.994    0.320   -0.220    0.673
##     coplan_0c (a2)    0.337    0.073    4.637    0.000    0.195    0.479
##     mvpagt_0c (a3)    0.001    0.004    0.313    0.754   -0.007    0.009
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    3.671    3.086    1.189    0.234   -2.378    9.721
##     coplan_0c (c2)    1.012    1.095    0.924    0.356   -1.135    3.158
##     mvpagt_0c (c3)    0.720    0.056   12.838    0.000    0.610    0.830
##     copln_18c (b1)   -0.158    1.231   -0.128    0.898   -2.571    2.255
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     coplan_0c        -0.018    0.051   -0.359    0.720   -0.118    0.081
##     mvpaagt_0c       -0.655    0.936   -0.700    0.484   -2.490    1.180
##   coplan_0c ~~                                                          
##     mvpaagt_0c        5.368    2.977    1.803    0.071   -0.466   11.202
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_18c       -0.124    0.156   -0.797    0.425   -0.429    0.181
##    .mvpaagt_24       36.679    2.182   16.812    0.000   32.403   40.955
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     coplan_0c        -0.001    0.102   -0.010    0.992   -0.200    0.198
##     mvpaagt_0c       -0.149    1.871   -0.080    0.937   -3.816    3.518
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_18c        1.906    0.221    8.631    0.000    1.473    2.338
##    .mvpaagt_24      297.219   37.126    8.006    0.000  224.454  369.984
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     coplan_0c         2.484    0.227   10.960    0.000    2.040    2.928
##     mvpaagt_0c      815.819   75.868   10.753    0.000  667.122  964.517
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.036    0.280   -0.128    0.898   -0.585    0.513
##     total             3.635    3.073    1.183    0.237   -2.388    9.658

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                             est      se     R     2.5%    97.5%
## a1                       0.2263  0.2254 20000  -0.2083   0.6717
## a2                       0.3369  0.0731 20000   0.1928   0.4781
## a3                       0.0013  0.0040 20000  -0.0067   0.0092
## c1                       3.6712  3.0932 20000  -2.3896   9.7419
## c2                       1.0115  1.0960 20000  -1.1419   3.1592
## c3                       0.7200  0.0564 20000   0.6086   0.8293
## b1                      -0.1582  1.2270 20000  -2.5799   2.2287
## groupfu~~coplan_0c      -0.0182  0.0505 20000  -0.1178   0.0803
## groupfu~~mvpaagt_0c     -0.6548  0.9316 20000  -2.5155   1.1378
## coplan_0c~~mvpaagt_0c    5.3682  2.9821 20000  -0.4981  11.1437
## coplan_18c~~coplan_18c   1.9056  0.2226 20000   1.4706   2.3418
## mvpaagt_24~~mvpaagt_24 297.2190 37.1947 20000 225.2030 370.8251
## groupfu~~groupfu         0.2499  0.0228 20000   0.2056   0.2943
## coplan_0c~~coplan_0c     2.4839  0.2272 20000   2.0433   2.9379
## mvpaagt_0c~~mvpaagt_0c 815.8194 75.6252 20000 666.7734 962.0548
## coplan_18c~1            -0.1241  0.1552 20000  -0.4298   0.1782
## mvpaagt_24~1            36.6790  2.1626 20000  32.4812  40.9928
## groupfu~1                0.5104  0.0324 20000   0.4460   0.5730
## coplan_0c~1             -0.0010  0.1023 20000  -0.2028   0.1996
## mvpaagt_0c~1            -0.1488  1.8668 20000  -3.7777   3.5535
## ind                     -0.0358  0.3930 20000  -0.9008   0.7904
## total                    3.6355  3.0958 20000  -2.3743   9.7203

Maintenance Self-Efficacy at 12 Months

model <- '
# Direct Effects
aufswe_12c ~ a1*groupfu + a2*aufswe_0c + a3*mvpaagt_0c
mvpaagt_24 ~ c1*groupfu + c2*aufswe_0c + c3*mvpaagt_0c + b1*aufswe_12c

# Covariances
groupfu ~~ aufswe_0c + mvpaagt_0c 
aufswe_0c ~~ mvpaagt_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 78 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         7
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   aufswe_12c ~                                                          
##     groupfu   (a1)   -0.179    0.160   -1.118    0.264   -0.492    0.135
##     aufswe_0c (a2)    0.134    0.069    1.953    0.051   -0.000    0.269
##     mvpagt_0c (a3)   -0.001    0.003   -0.346    0.729   -0.007    0.005
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    4.281    3.093    1.384    0.166   -1.781   10.342
##     aufswe_0c (c2)    1.594    1.318    1.209    0.227   -0.990    4.178
##     mvpagt_0c (c3)    0.723    0.055   13.103    0.000    0.614    0.831
##     aufsw_12c (b1)    1.018    1.496    0.680    0.496   -1.915    3.950
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     aufswe_0c        -0.066    0.038   -1.767    0.077   -0.140    0.007
##     mvpaagt_0c       -0.681    0.937   -0.727    0.467   -2.516    1.155
##   aufswe_0c ~~                                                          
##     mvpaagt_0c        1.357    2.166    0.626    0.531   -2.889    5.602
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_12c        0.093    0.108    0.854    0.393   -0.120    0.305
##    .mvpaagt_24       36.480    2.174   16.781    0.000   32.219   40.740
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     aufswe_0c         0.000    0.075    0.000    1.000   -0.147    0.147
##     mvpaagt_0c       -0.108    1.872   -0.058    0.954   -3.777    3.560
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_12c        1.001    0.111    9.000    0.000    0.783    1.219
##    .mvpaagt_24      294.464   36.783    8.005    0.000  222.370  366.557
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     aufswe_0c         1.347    0.123   10.977    0.000    1.107    1.588
##     mvpaagt_0c      816.275   75.928   10.751    0.000  667.459  965.091
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.182    0.314   -0.581    0.561   -0.797    0.432
##     total             4.099    3.083    1.329    0.184   -1.944   10.141

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                             est      se     R     2.5%    97.5%
## a1                      -0.1789  0.1584 20000  -0.4845   0.1337
## a2                       0.1341  0.0687 20000  -0.0024   0.2670
## a3                      -0.0010  0.0029 20000  -0.0066   0.0047
## c1                       4.2806  3.0947 20000  -1.7996  10.3103
## c2                       1.5936  1.3115 20000  -0.9344   4.1954
## c3                       0.7225  0.0553 20000   0.6140   0.8283
## b1                       1.0176  1.4910 20000  -1.9167   3.9841
## groupfu~~aufswe_0c      -0.0665  0.0374 20000  -0.1394   0.0062
## groupfu~~mvpaagt_0c     -0.6808  0.9443 20000  -2.5167   1.1960
## aufswe_0c~~mvpaagt_0c    1.3568  2.1686 20000  -2.8457   5.6180
## aufswe_12c~~aufswe_12c   1.0014  0.1122 20000   0.7822   1.2210
## mvpaagt_24~~mvpaagt_24 294.4636 36.8514 20000 221.5368 365.8127
## groupfu~~groupfu         0.2499  0.0228 20000   0.2055   0.2943
## aufswe_0c~~aufswe_0c     1.3474  0.1231 20000   1.1080   1.5967
## mvpaagt_0c~~mvpaagt_0c 816.2747 75.6854 20000 669.9071 965.4336
## aufswe_12c~1             0.0926  0.1082 20000  -0.1234   0.3048
## mvpaagt_24~1            36.4799  2.1593 20000  32.1729  40.6838
## groupfu~1                0.5104  0.0324 20000   0.4460   0.5735
## aufswe_0c~1              0.0000  0.0752 20000  -0.1486   0.1474
## mvpaagt_0c~1            -0.1083  1.8675 20000  -3.8205   3.5283
## ind                     -0.1821  0.3904 20000  -1.1329   0.4837
## total                    4.0986  3.0955 20000  -2.0218  10.1465

Maintenance Self-Efficacy at 18 Months

model <- '
# Direct Effects
aufswe_18c ~ a1*groupfu + a2*aufswe_0c + a3*mvpaagt_0c
mvpaagt_24 ~ c1*groupfu + c2*aufswe_0c + c3*mvpaagt_0c + b1*aufswe_18c

# Covariances
groupfu ~~ aufswe_0c + mvpaagt_0c 
aufswe_0c ~~ mvpaagt_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 76 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         6
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   aufswe_18c ~                                                          
##     groupfu   (a1)   -0.003    0.171   -0.020    0.984   -0.339    0.333
##     aufswe_0c (a2)    0.258    0.073    3.543    0.000    0.115    0.401
##     mvpagt_0c (a3)    0.002    0.003    0.736    0.462   -0.004    0.008
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    3.688    3.085    1.195    0.232   -2.359    9.734
##     aufswe_0c (c2)    1.309    1.342    0.976    0.329   -1.321    3.940
##     mvpagt_0c (c3)    0.712    0.056   12.814    0.000    0.603    0.821
##     aufsw_18c (b1)    1.852    1.474    1.257    0.209   -1.036    4.741
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     aufswe_0c        -0.066    0.038   -1.767    0.077   -0.140    0.007
##     mvpaagt_0c       -0.671    0.936   -0.716    0.474   -2.506    1.165
##   aufswe_0c ~~                                                          
##     mvpaagt_0c        1.355    2.166    0.626    0.531   -2.889    5.600
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_18c        0.020    0.117    0.174    0.862   -0.210    0.250
##    .mvpaagt_24       36.825    2.173   16.944    0.000   32.566   41.085
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     aufswe_0c        -0.000    0.075   -0.000    1.000   -0.147    0.147
##     mvpaagt_0c       -0.102    1.871   -0.054    0.957   -3.770    3.566
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_18c        1.100    0.126    8.721    0.000    0.853    1.347
##    .mvpaagt_24      292.198   36.504    8.004    0.000  220.651  363.746
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     aufswe_0c         1.347    0.123   10.977    0.000    1.107    1.588
##     mvpaagt_0c      815.979   75.893   10.752    0.000  667.231  964.728
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.006    0.317   -0.020    0.984   -0.629    0.616
##     total             3.681    3.099    1.188    0.235   -2.393    9.755

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                             est      se     R     2.5%    97.5%
## a1                      -0.0034  0.1697 20000  -0.3295   0.3346
## a2                       0.2584  0.0736 20000   0.1143   0.4051
## a3                       0.0022  0.0030 20000  -0.0037   0.0082
## c1                       3.6878  3.0754 20000  -2.3112   9.7821
## c2                       1.3095  1.3321 20000  -1.3602   3.8921
## c3                       0.7122  0.0558 20000   0.6021   0.8203
## b1                       1.8525  1.4706 20000  -1.0669   4.7137
## groupfu~~aufswe_0c      -0.0665  0.0375 20000  -0.1393   0.0069
## groupfu~~mvpaagt_0c     -0.6707  0.9440 20000  -2.4993   1.1962
## aufswe_0c~~mvpaagt_0c    1.3554  2.1670 20000  -2.8517   5.6284
## aufswe_18c~~aufswe_18c   1.0997  0.1264 20000   0.8522   1.3561
## mvpaagt_24~~mvpaagt_24 292.1982 36.5723 20000 221.3604 364.5840
## groupfu~~groupfu         0.2499  0.0228 20000   0.2053   0.2945
## aufswe_0c~~aufswe_0c     1.3474  0.1238 20000   1.1052   1.5898
## mvpaagt_0c~~mvpaagt_0c 815.9792 75.6510 20000 666.8912 962.2855
## aufswe_18c~1             0.0204  0.1168 20000  -0.2099   0.2466
## mvpaagt_24~1            36.8253  2.1744 20000  32.6181  41.1158
## groupfu~1                0.5104  0.0323 20000   0.4475   0.5746
## aufswe_0c~1              0.0000  0.0751 20000  -0.1484   0.1460
## mvpaagt_0c~1            -0.1019  1.8675 20000  -3.7276   3.6061
## ind                     -0.0064  0.4060 20000  -0.8734   0.8820
## total                    3.6814  3.0994 20000  -2.3639   9.8243

Recovery Self-Efficacy at 12 Months

model <- '
# Direct Effects
wieswe_12c ~ a1*groupfu + a2*wieswe_0c + a3*mvpaagt_0c
mvpaagt_24 ~ c1*groupfu + c2*wieswe_0c + c3*mvpaagt_0c + b1*wieswe_12c

# Covariances
groupfu ~~ wieswe_0c + mvpaagt_0c 
wieswe_0c ~~ mvpaagt_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 73 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         8
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wieswe_12c ~                                                          
##     groupfu   (a1)    0.233    0.149    1.565    0.118   -0.059    0.524
##     wieswe_0c (a2)    0.257    0.073    3.533    0.000    0.115    0.400
##     mvpagt_0c (a3)    0.001    0.003    0.537    0.591   -0.004    0.007
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    3.040    3.062    0.993    0.321   -2.962    9.041
##     wieswe_0c (c2)    0.962    1.457    0.661    0.509   -1.892    3.817
##     mvpagt_0c (c3)    0.713    0.055   13.015    0.000    0.605    0.820
##     wiesw_12c (b1)    3.276    1.578    2.076    0.038    0.183    6.369
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     wieswe_0c        -0.062    0.033   -1.895    0.058   -0.126    0.002
##     mvpaagt_0c       -0.680    0.936   -0.726    0.468   -2.515    1.156
##   wieswe_0c ~~                                                          
##     mvpaagt_0c        3.668    1.883    1.948    0.051   -0.023    7.360
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_12c       -0.091    0.101   -0.898    0.369   -0.288    0.107
##    .mvpaagt_24       37.071    2.142   17.303    0.000   32.872   41.270
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     wieswe_0c         0.001    0.065    0.008    0.994   -0.126    0.127
##     mvpaagt_0c       -0.146    1.871   -0.078    0.938   -3.813    3.522
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_12c        0.868    0.096    9.003    0.000    0.679    1.057
##    .mvpaagt_24      285.840   35.707    8.005    0.000  215.855  355.824
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     wieswe_0c         1.006    0.092   10.955    0.000    0.826    1.186
##     mvpaagt_0c      816.250   75.919   10.752    0.000  667.452  965.049
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.763    0.611    1.249    0.212   -0.434    1.960
##     total             3.802    3.064    1.241    0.215   -2.203    9.807

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                             est      se     R     2.5%    97.5%
## a1                       0.2328  0.1474 20000  -0.0573   0.5138
## a2                       0.2573  0.0734 20000   0.1130   0.4020
## a3                       0.0014  0.0027 20000  -0.0038   0.0067
## c1                       3.0397  3.0629 20000  -2.9869   8.9741
## c2                       0.9625  1.4546 20000  -1.8407   3.8388
## c3                       0.7127  0.0550 20000   0.6062   0.8211
## b1                       3.2757  1.5645 20000   0.2235   6.3916
## groupfu~~wieswe_0c      -0.0618  0.0323 20000  -0.1253   0.0012
## groupfu~~mvpaagt_0c     -0.6796  0.9418 20000  -2.5393   1.1464
## wieswe_0c~~mvpaagt_0c    3.6684  1.8791 20000  -0.0072   7.3737
## wieswe_12c~~wieswe_12c   0.8681  0.0967 20000   0.6721   1.0570
## mvpaagt_24~~mvpaagt_24 285.8399 35.7753 20000 216.7302 356.5199
## groupfu~~groupfu         0.2499  0.0228 20000   0.2054   0.2944
## wieswe_0c~~wieswe_0c     1.0060  0.0925 20000   0.8240   1.1861
## mvpaagt_0c~~mvpaagt_0c 816.2503 75.6758 20000 669.8882 965.3518
## wieswe_12c~1            -0.0906  0.1002 20000  -0.2871   0.1080
## mvpaagt_24~1            37.0707  2.1308 20000  32.8627  41.1936
## groupfu~1                0.5104  0.0324 20000   0.4475   0.5744
## wieswe_0c~1              0.0005  0.0650 20000  -0.1277   0.1275
## mvpaagt_0c~1            -0.1457  1.8724 20000  -3.8545   3.5063
## ind                      0.7626  0.6462 20000  -0.2109   2.2824
## total                    3.8024  3.0649 20000  -2.2548   9.7887

Recovery Self-Efficacy at 18 Months

model <- '
# Direct Effects
wieswe_18c ~ a1*groupfu + a2*wieswe_0c + a3*mvpaagt_0c
mvpaagt_24 ~ c1*groupfu + c2*wieswe_0c + c3*mvpaagt_0c + b1*wieswe_18c

# Covariances
groupfu ~~ wieswe_0c + mvpaagt_0c 
wieswe_0c ~~ mvpaagt_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 77 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         7
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wieswe_18c ~                                                          
##     groupfu   (a1)   -0.040    0.165   -0.239    0.811   -0.364    0.285
##     wieswe_0c (a2)    0.287    0.084    3.434    0.001    0.123    0.451
##     mvpagt_0c (a3)    0.004    0.003    1.356    0.175   -0.002    0.010
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    4.010    3.052    1.314    0.189   -1.973    9.992
##     wieswe_0c (c2)    1.239    1.463    0.847    0.397   -1.629    4.106
##     mvpagt_0c (c3)    0.710    0.056   12.790    0.000    0.601    0.819
##     wiesw_18c (b1)    2.256    1.550    1.455    0.146   -0.782    5.295
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     wieswe_0c        -0.062    0.033   -1.895    0.058   -0.126    0.002
##     mvpaagt_0c       -0.680    0.936   -0.726    0.468   -2.515    1.155
##   wieswe_0c ~~                                                          
##     mvpaagt_0c        3.655    1.883    1.941    0.052   -0.036    7.346
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_18c        0.035    0.113    0.311    0.755   -0.187    0.258
##    .mvpaagt_24       36.703    2.153   17.044    0.000   32.483   40.924
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     wieswe_0c         0.001    0.065    0.008    0.994   -0.126    0.127
##     mvpaagt_0c       -0.133    1.871   -0.071    0.944   -3.800    3.535
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_18c        1.012    0.117    8.663    0.000    0.783    1.241
##    .mvpaagt_24      290.581   36.295    8.006    0.000  219.444  361.719
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     wieswe_0c         1.006    0.092   10.955    0.000    0.826    1.186
##     mvpaagt_0c      816.112   75.907   10.751    0.000  667.337  964.887
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.089    0.377   -0.237    0.813   -0.828    0.649
##     total             3.921    3.072    1.276    0.202   -2.099    9.941

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                             est      se     R     2.5%    97.5%
## a1                      -0.0396  0.1641 20000  -0.3565   0.2854
## a2                       0.2872  0.0844 20000   0.1221   0.4528
## a3                       0.0040  0.0030 20000  -0.0019   0.0098
## c1                       4.0099  3.0456 20000  -1.9491  10.0519
## c2                       1.2389  1.4520 20000  -1.6397   4.0626
## c3                       0.7099  0.0557 20000   0.5999   0.8175
## b1                       2.2563  1.5450 20000  -0.7909   5.2710
## groupfu~~wieswe_0c      -0.0618  0.0323 20000  -0.1251   0.0014
## groupfu~~mvpaagt_0c     -0.6801  0.9455 20000  -2.5431   1.1622
## wieswe_0c~~mvpaagt_0c    3.6547  1.8804 20000  -0.0287   7.3308
## wieswe_18c~~wieswe_18c   1.0124  0.1172 20000   0.7831   1.2501
## mvpaagt_24~~mvpaagt_24 290.5813 36.3647 20000 220.3289 362.4201
## groupfu~~groupfu         0.2499  0.0229 20000   0.2053   0.2944
## wieswe_0c~~wieswe_0c     1.0060  0.0925 20000   0.8260   1.1874
## mvpaagt_0c~~mvpaagt_0c 816.1120 75.6637 20000 669.7511 965.1955
## wieswe_18c~1             0.0353  0.1124 20000  -0.1861   0.2537
## mvpaagt_24~1            36.7033  2.1517 20000  32.5431  40.9768
## groupfu~1                0.5104  0.0324 20000   0.4459   0.5732
## wieswe_0c~1              0.0005  0.0652 20000  -0.1291   0.1263
## mvpaagt_0c~1            -0.1325  1.8724 20000  -3.7867   3.5637
## ind                     -0.0893  0.4558 20000  -1.0998   0.8241
## total                    3.9206  3.0761 20000  -2.0976  10.0527

Action Control at 12 Months

model <- '
# Direct Effects
hk_12c ~ a1*groupfu + a2*hk_0c + a3*mvpaagt_0c
mvpaagt_24 ~ c1*groupfu + c2*hk_0c + c3*mvpaagt_0c + b1*hk_12c

# Covariances
groupfu ~~ hk_0c + mvpaagt_0c 
hk_0c ~~ mvpaagt_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 81 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         7
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   hk_12c ~                                                              
##     groupfu   (a1)    0.334    0.181    1.849    0.064   -0.020    0.689
##     hk_0c     (a2)    0.402    0.064    6.319    0.000    0.277    0.527
##     mvpagt_0c (a3)   -0.001    0.003   -0.453    0.650   -0.008    0.005
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    3.275    3.110    1.053    0.292   -2.820    9.369
##     hk_0c     (c2)    0.273    1.177    0.232    0.817   -2.034    2.580
##     mvpagt_0c (c3)    0.731    0.055   13.197    0.000    0.623    0.840
##     hk_12c    (b1)    1.464    1.416    1.034    0.301   -1.311    4.239
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     hk_0c            -0.040    0.046   -0.874    0.382   -0.130    0.050
##     mvpaagt_0c       -0.666    0.936   -0.711    0.477   -2.501    1.169
##   hk_0c ~~                                                              
##     mvpaagt_0c        2.340    2.679    0.873    0.382   -2.911    7.592
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_12c           -0.175    0.124   -1.419    0.156   -0.418    0.067
##    .mvpaagt_24       36.645    2.181   16.805    0.000   32.371   40.919
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     hk_0c            -0.000    0.091   -0.000    1.000   -0.179    0.179
##     mvpaagt_0c       -0.148    1.871   -0.079    0.937   -3.814    3.519
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_12c            1.288    0.144    8.973    0.000    1.007    1.569
##    .mvpaagt_24      295.688   36.955    8.001    0.000  223.258  368.117
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     hk_0c             2.015    0.184   10.977    0.000    1.655    2.375
##     mvpaagt_0c      815.505   75.823   10.755    0.000  666.895  964.115
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.490    0.543    0.902    0.367   -0.574    1.553
##     total             3.764    3.084    1.221    0.222   -2.281    9.809

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                             est      se     R     2.5%    97.5%
## a1                       0.3345  0.1790 20000  -0.0111   0.6891
## a2                       0.4019  0.0639 20000   0.2780   0.5290
## a3                      -0.0015  0.0033 20000  -0.0080   0.0049
## c1                       3.2746  3.1154 20000  -2.8195   9.3671
## c2                       0.2727  1.1703 20000  -2.0358   2.5768
## c3                       0.7312  0.0557 20000   0.6236   0.8413
## b1                       1.4638  1.4114 20000  -1.2913   4.2449
## groupfu~~hk_0c          -0.0400  0.0455 20000  -0.1293   0.0485
## groupfu~~mvpaagt_0c     -0.6659  0.9396 20000  -2.5167   1.1364
## hk_0c~~mvpaagt_0c        2.3403  2.6812 20000  -2.9730   7.5614
## hk_12c~~hk_12c           1.2881  0.1447 20000   1.0050   1.5710
## mvpaagt_24~~mvpaagt_24 295.6879 37.0253 20000 224.1565 368.8794
## groupfu~~groupfu         0.2499  0.0229 20000   0.2054   0.2945
## hk_0c~~hk_0c             2.0150  0.1840 20000   1.6571   2.3864
## mvpaagt_0c~~mvpaagt_0c 815.5047 75.5796 20000 669.3128 964.4295
## hk_12c~1                -0.1755  0.1232 20000  -0.4181   0.0651
## mvpaagt_24~1            36.6452  2.1613 20000  32.3734  40.8308
## groupfu~1                0.5104  0.0324 20000   0.4457   0.5731
## hk_0c~1                  0.0000  0.0919 20000  -0.1818   0.1802
## mvpaagt_0c~1            -0.1478  1.8665 20000  -3.8579   3.4673
## ind                      0.4896  0.5902 20000  -0.4395   1.8763
## total                    3.7642  3.1066 20000  -2.3407   9.8237

Action Control at 18 Months

model <- '
# Direct Effects
hk_18c ~ a1*groupfu + a2*hk_0c + a3*mvpaagt_0c
mvpaagt_24 ~ c1*groupfu + c2*hk_0c + c3*mvpaagt_0c + b1*hk_18c

# Covariances
groupfu ~~ hk_0c + mvpaagt_0c 
hk_0c ~~ mvpaagt_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 82 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         6
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   hk_18c ~                                                              
##     groupfu   (a1)    0.272    0.170    1.605    0.108   -0.060    0.605
##     hk_0c     (a2)    0.371    0.060    6.197    0.000    0.254    0.488
##     mvpagt_0c (a3)    0.005    0.003    1.747    0.081   -0.001    0.011
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    3.208    3.144    1.021    0.307   -2.953    9.370
##     hk_0c     (c2)    0.328    1.187    0.277    0.782   -1.999    2.656
##     mvpagt_0c (c3)    0.718    0.056   12.841    0.000    0.609    0.828
##     hk_18c    (b1)    1.425    1.579    0.902    0.367   -1.670    4.519
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     hk_0c            -0.040    0.046   -0.874    0.382   -0.130    0.050
##     mvpaagt_0c       -0.669    0.937   -0.714    0.475   -2.505    1.167
##   hk_0c ~~                                                              
##     mvpaagt_0c        2.356    2.681    0.879    0.380   -2.898    7.610
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_18c           -0.154    0.116   -1.330    0.184   -0.382    0.073
##    .mvpaagt_24       36.818    2.201   16.728    0.000   32.504   41.132
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     hk_0c             0.000    0.091    0.000    1.000   -0.179    0.179
##     mvpaagt_0c       -0.148    1.872   -0.079    0.937   -3.816    3.521
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_18c            1.072    0.123    8.690    0.000    0.830    1.314
##    .mvpaagt_24      295.943   36.942    8.011    0.000  223.539  368.348
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     hk_0c             2.015    0.184   10.977    0.000    1.655    2.375
##     mvpaagt_0c      816.235   75.926   10.750    0.000  667.423  965.047
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.388    0.498    0.779    0.436   -0.588    1.364
##     total             3.596    3.091    1.163    0.245   -2.462    9.655

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                             est      se     R     2.5%    97.5%
## a1                       0.2722  0.1682 20000  -0.0504   0.6052
## a2                       0.3709  0.0603 20000   0.2538   0.4902
## a3                       0.0052  0.0030 20000  -0.0007   0.0111
## c1                       3.2084  3.1489 20000  -2.9486   9.3376
## c2                       0.3284  1.1816 20000  -1.9493   2.7039
## c3                       0.7182  0.0560 20000   0.6083   0.8261
## b1                       1.4246  1.5698 20000  -1.6118   4.5120
## groupfu~~hk_0c          -0.0400  0.0456 20000  -0.1280   0.0502
## groupfu~~mvpaagt_0c     -0.6690  0.9440 20000  -2.5246   1.1930
## hk_0c~~mvpaagt_0c        2.3557  2.6824 20000  -2.9433   7.5948
## hk_18c~~hk_18c           1.0723  0.1244 20000   0.8290   1.3157
## mvpaagt_24~~mvpaagt_24 295.9434 37.0124 20000 224.4358 369.0985
## groupfu~~groupfu         0.2499  0.0228 20000   0.2054   0.2943
## hk_0c~~hk_0c             2.0150  0.1840 20000   1.6431   2.3714
## mvpaagt_0c~~mvpaagt_0c 816.2350 75.6826 20000 669.8440 965.3622
## hk_18c~1                -0.1544  0.1152 20000  -0.3799   0.0699
## mvpaagt_24~1            36.8179  2.1824 20000  32.4708  41.0959
## groupfu~1                0.5104  0.0324 20000   0.4462   0.5728
## hk_0c~1                  0.0000  0.0919 20000  -0.1802   0.1815
## mvpaagt_0c~1            -0.1478  1.8681 20000  -3.7923   3.5626
## ind                      0.3878  0.5616 20000  -0.4936   1.7592
## total                    3.5962  3.1115 20000  -2.5018   9.6659

Collaborative Planning at 18 Months

model <- '
# Direct Effects
collimpint_18c ~ a1*groupfu + a2*collimpint_0c + a3*mvpaagt_0c
mvpaagt_24 ~ c1*groupfu + c2*collimpint_0c + c3*mvpaagt_0c + b1*collimpint_18c

# Covariances
groupfu ~~ collimpint_0c + mvpaagt_0c 
collimpint_0c ~~ mvpaagt_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 93 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                        13
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   collimpint_18c ~                                                      
##     groupfu   (a1)    0.532    0.405    1.312    0.189   -0.262    1.326
##     cllmpnt_0 (a2)    0.411    0.101    4.060    0.000    0.212    0.609
##     mvpagt_0c (a3)    0.011    0.006    1.759    0.079   -0.001    0.024
##   mvpaagt_24 ~                                                          
##     groupfu   (c1)    3.689    3.216    1.147    0.251   -2.615    9.993
##     cllmpnt_0 (c2)   -0.652    1.184   -0.551    0.582   -2.973    1.668
##     mvpagt_0c (c3)    0.729    0.057   12.776    0.000    0.618    0.841
##     cllmpn_18 (b1)    0.387    1.378    0.281    0.779   -2.314    3.088
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     collimpint_0c     0.008    0.092    0.086    0.931   -0.172    0.188
##     mvpaagt_0c       -0.677    0.936   -0.723    0.470   -2.513    1.158
##   collimpint_0c ~~                                                      
##     mvpaagt_0c        5.381    5.055    1.065    0.287   -4.526   15.289
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_18c   -0.308    0.258   -1.197    0.231   -0.814    0.197
##    .mvpaagt_24       36.633    2.233   16.407    0.000   32.257   41.009
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     collimpint_0c    -0.100    0.183   -0.544    0.586   -0.459    0.260
##     mvpaagt_0c       -0.120    1.871   -0.064    0.949   -3.788    3.548
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_18c    2.839    0.472    6.013    0.000    1.913    3.764
##    .mvpaagt_24      297.519   37.427    7.949    0.000  224.163  370.875
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     collimpint_0c     4.385    0.550    7.978    0.000    3.308    5.462
##     mvpaagt_0c      816.114   75.897   10.753    0.000  667.358  964.870
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.206    0.733    0.281    0.779   -1.231    1.642
##     total             3.895    3.127    1.246    0.213   -2.234   10.023

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                     est      se     R     2.5%    97.5%
## a1                               0.5316  0.4075 20000  -0.2721   1.3342
## a2                               0.4107  0.1022 20000   0.2128   0.6137
## a3                               0.0114  0.0065 20000  -0.0014   0.0242
## c1                               3.6888  3.2189 20000  -2.5267  10.0491
## c2                              -0.6523  1.1810 20000  -2.9644   1.6442
## c3                               0.7294  0.0568 20000   0.6184   0.8408
## b1                               0.3871  1.3755 20000  -2.2938   3.0797
## groupfu~~collimpint_0c           0.0079  0.0921 20000  -0.1706   0.1904
## groupfu~~mvpaagt_0c             -0.6772  0.9324 20000  -2.4682   1.1909
## collimpint_0c~~mvpaagt_0c        5.3814  5.0553 20000  -4.5009  15.3803
## collimpint_18c~~collimpint_18c   2.8387  0.4724 20000   1.8972   3.7508
## mvpaagt_24~~mvpaagt_24         297.5191 37.4990 20000 225.0733 371.6466
## groupfu~~groupfu                 0.2499  0.0228 20000   0.2056   0.2942
## collimpint_0c~~collimpint_0c     4.3848  0.5441 20000   3.3024   5.4243
## mvpaagt_0c~~mvpaagt_0c         816.1141 75.6536 20000 669.7736 965.1739
## collimpint_18c~1                -0.3084  0.2587 20000  -0.8135   0.2016
## mvpaagt_24~1                    36.6332  2.2273 20000  32.2212  40.9349
## groupfu~1                        0.5104  0.0323 20000   0.4473   0.5748
## collimpint_0c~1                 -0.0997  0.1857 20000  -0.4640   0.2646
## mvpaagt_0c~1                    -0.1199  1.8672 20000  -3.7466   3.5767
## ind                              0.2058  0.9264 20000  -1.7919   2.2061
## total                            3.8946  3.1782 20000  -2.3844  10.0751

S-Table 8

Action Planning at 12 Months

model <- '
# Direct Effects
acplan_12c ~ a1*groupfu + a2*acplan_0c + a3*muskeltraint_0c
muskeltraint_24 ~ c1*groupfu + c2*acplan_0c + c3*muskeltraint_0c + b1*acplan_12c

# Covariances
groupfu ~~ acplan_0c + muskeltraint_0c 
acplan_0c ~~ muskeltraint_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 81 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         4
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   acplan_12c ~                                                           
##     groupfu   (a1)     0.532    0.244    2.178    0.029    0.053    1.010
##     acplan_0c (a2)     0.381    0.067    5.697    0.000    0.250    0.511
##     mskltrn_0 (a3)     0.003    0.005    0.522    0.602   -0.008    0.014
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     3.289    3.706    0.887    0.375   -3.974   10.552
##     acplan_0c (c2)     2.997    1.125    2.664    0.008    0.792    5.201
##     mskltrn_0 (c3)     0.494    0.074    6.639    0.000    0.348    0.639
##     acpln_12c (b1)    -0.402    1.247   -0.322    0.747   -2.846    2.042
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     acplan_0c        -0.007    0.057   -0.132    0.895   -0.119    0.104
##     muskeltrant_0c   -1.338    0.943   -1.418    0.156   -3.186    0.511
##   acplan_0c ~~                                                          
##     muskeltrant_0c    7.249    3.340    2.170    0.030    0.703   13.796
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_12c       -0.217    0.167   -1.301    0.193   -0.543    0.110
##    .muskeltrant_24   21.606    2.559    8.445    0.000   16.592   26.621
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     acplan_0c         0.000    0.113    0.000    1.000   -0.222    0.222
##     muskeltrant_0c    0.000    1.879    0.000    1.000   -3.682    3.682
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_12c        2.330    0.261    8.916    0.000    1.818    2.842
##    .muskeltrant_24  564.328   61.036    9.246    0.000  444.700  683.956
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     acplan_0c         3.099    0.282   10.977    0.000    2.545    3.652
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.214    0.667   -0.321    0.749   -1.520    1.093
##     total             3.075    3.653    0.842    0.400   -4.085   10.235

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                 0.5315  0.2447 20000   0.0572    1.0237
## a2                                 0.3805  0.0670 20000   0.2510    0.5128
## a3                                 0.0029  0.0055 20000  -0.0080    0.0138
## c1                                 3.2887  3.7010 20000  -3.9461   10.5762
## c2                                 2.9965  1.1244 20000   0.7777    5.1858
## c3                                 0.4935  0.0746 20000   0.3491    0.6415
## b1                                -0.4019  1.2392 20000  -2.8172    2.0323
## groupfu~~acplan_0c                -0.0075  0.0564 20000  -0.1163    0.1035
## groupfu~~muskeltraint_0c          -1.3377  0.9365 20000  -3.2050    0.4617
## acplan_0c~~muskeltraint_0c         7.2492  3.3392 20000   0.6737   13.7777
## acplan_12c~~acplan_12c             2.3300  0.2634 20000   1.8157    2.8459
## muskeltraint_24~~muskeltraint_24 564.3281 61.1511 20000 446.0894  685.2560
## groupfu~~groupfu                   0.2499  0.0228 20000   0.2056    0.2945
## acplan_0c~~acplan_0c               3.0985  0.2792 20000   2.5459    3.6404
## muskeltraint_0c~~muskeltraint_0c 850.7022 77.2490 20000 701.2858 1002.9016
## acplan_12c~1                      -0.2167  0.1670 20000  -0.5483    0.1084
## muskeltraint_24~1                 21.6063  2.5558 20000  16.5829   26.6579
## groupfu~1                          0.5104  0.0324 20000   0.4476    0.5748
## acplan_0c~1                        0.0000  0.1142 20000  -0.2233    0.2249
## muskeltraint_0c~1                  0.0000  1.8749 20000  -3.7317    3.6430
## ind                               -0.2136  0.7309 20000  -1.8158    1.2342
## total                              3.0750  3.6529 20000  -4.0548   10.3234

Action Planning at 18 Months

model <- '
# Direct Effects
acplan_18c ~ a1*groupfu + a2*acplan_0c + a3*muskeltraint_0c
muskeltraint_24 ~ c1*groupfu + c2*acplan_0c + c3*muskeltraint_0c + b1*acplan_18c

# Covariances
groupfu ~~ acplan_0c + muskeltraint_0c 
acplan_0c ~~ muskeltraint_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 74 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         4
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   acplan_18c ~                                                           
##     groupfu   (a1)     0.422    0.255    1.652    0.099   -0.079    0.922
##     acplan_0c (a2)     0.202    0.072    2.805    0.005    0.061    0.343
##     mskltrn_0 (a3)     0.012    0.006    2.061    0.039    0.001    0.024
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     2.627    3.720    0.706    0.480   -4.664    9.918
##     acplan_0c (c2)     2.643    1.061    2.492    0.013    0.564    4.722
##     mskltrn_0 (c3)     0.482    0.076    6.321    0.000    0.332    0.631
##     acpln_18c (b1)     0.858    1.331    0.645    0.519   -1.751    3.467
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     acplan_0c        -0.007    0.057   -0.132    0.895   -0.119    0.104
##     muskeltrant_0c   -1.338    0.943   -1.418    0.156   -3.186    0.511
##   acplan_0c ~~                                                          
##     muskeltrant_0c    7.249    3.340    2.170    0.030    0.703   13.796
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_18c       -0.163    0.175   -0.935    0.350   -0.506    0.179
##    .muskeltrant_24   21.846    2.558    8.540    0.000   16.832   26.859
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     acplan_0c        -0.000    0.113   -0.000    1.000   -0.222    0.222
##     muskeltrant_0c    0.000    1.879    0.000    1.000   -3.682    3.682
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_18c        2.433    0.280    8.689    0.000    1.884    2.982
##    .muskeltrant_24  563.040   60.932    9.241    0.000  443.616  682.464
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     acplan_0c         3.099    0.282   10.977    0.000    2.545    3.652
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.362    0.615    0.589    0.556   -0.843    1.567
##     total             2.989    3.656    0.817    0.414   -4.177   10.155

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                 0.4216  0.2533 20000  -0.0664    0.9244
## a2                                 0.2019  0.0723 20000   0.0620    0.3449
## a3                                 0.0121  0.0059 20000   0.0006    0.0237
## c1                                 2.6269  3.7218 20000  -4.6201    9.9139
## c2                                 2.6434  1.0499 20000   0.5788    4.6991
## c3                                 0.4816  0.0768 20000   0.3297    0.6304
## b1                                 0.8584  1.3149 20000  -1.7020    3.4274
## groupfu~~acplan_0c                -0.0075  0.0565 20000  -0.1172    0.1042
## groupfu~~muskeltraint_0c          -1.3377  0.9508 20000  -3.1806    0.5354
## acplan_0c~~muskeltraint_0c         7.2492  3.3464 20000   0.6440   13.7528
## acplan_18c~~acplan_18c             2.4328  0.2806 20000   1.8699    2.9783
## muskeltraint_24~~muskeltraint_24 563.0399 61.0467 20000 445.0036  683.7593
## groupfu~~groupfu                   0.2499  0.0228 20000   0.2056    0.2942
## acplan_0c~~acplan_0c               3.0985  0.2850 20000   2.5465    3.6600
## muskeltraint_0c~~muskeltraint_0c 850.7023 77.2487 20000 698.5276 1000.0821
## acplan_18c~1                      -0.1635  0.1739 20000  -0.5043    0.1748
## muskeltraint_24~1                 21.8456  2.5508 20000  16.8632   26.8389
## groupfu~1                          0.5104  0.0323 20000   0.4474    0.5748
## acplan_0c~1                        0.0000  0.1139 20000  -0.2216    0.2227
## muskeltraint_0c~1                  0.0000  1.8749 20000  -3.6441    3.7314
## ind                                0.3619  0.7041 20000  -0.7849    2.0961
## total                              2.9889  3.6862 20000  -4.2262   10.2327

Coping Planning at 12 Months

model <- '
# Direct Effects
coplan_12c ~ a1*groupfu + a2*coplan_0c + a3*muskeltraint_0c
muskeltraint_24 ~ c1*groupfu + c2*coplan_0c + c3*muskeltraint_0c + b1*coplan_12c

# Covariances
groupfu ~~ coplan_0c + muskeltraint_0c 
coplan_0c ~~ muskeltraint_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 50 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         5
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   coplan_12c ~                                                           
##     groupfu   (a1)     0.093    0.216    0.432    0.666   -0.329    0.516
##     coplan_0c (a2)     0.412    0.069    5.959    0.000    0.277    0.548
##     mskltrn_0 (a3)    -0.000    0.005   -0.018    0.985   -0.010    0.010
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     2.771    3.669    0.755    0.450   -4.420    9.961
##     coplan_0c (c2)     2.981    1.340    2.225    0.026    0.355    5.608
##     mskltrn_0 (c3)     0.477    0.076    6.277    0.000    0.328    0.625
##     copln_12c (b1)     0.163    1.415    0.115    0.908   -2.611    2.937
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     coplan_0c        -0.019    0.051   -0.374    0.709   -0.119    0.081
##     muskeltrant_0c   -1.338    0.943   -1.418    0.156   -3.186    0.511
##   coplan_0c ~~                                                          
##     muskeltrant_0c    8.266    3.008    2.748    0.006    2.370   14.161
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_12c       -0.040    0.148   -0.272    0.786   -0.329    0.249
##    .muskeltrant_24   21.799    2.559    8.520    0.000   16.785   26.814
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     coplan_0c         0.001    0.102    0.005    0.996   -0.199    0.200
##     muskeltrant_0c    0.000    1.879    0.000    1.000   -3.682    3.682
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_12c        1.844    0.206    8.968    0.000    1.441    2.246
##    .muskeltrant_24  568.829   61.527    9.245    0.000  448.237  689.420
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     coplan_0c         2.483    0.226   10.964    0.000    2.039    2.927
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.015    0.137    0.110    0.912   -0.254    0.284
##     total             2.786    3.669    0.759    0.448   -4.405    9.976

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                 0.0931  0.2133 20000  -0.3190    0.5158
## a2                                 0.4124  0.0695 20000   0.2748    0.5464
## a3                                -0.0001  0.0049 20000  -0.0098    0.0096
## c1                                 2.7707  3.6561 20000  -4.3445    9.9950
## c2                                 2.9811  1.3310 20000   0.3648    5.5841
## c3                                 0.4766  0.0762 20000   0.3300    0.6280
## b1                                 0.1628  1.4116 20000  -2.6413    2.9064
## groupfu~~coplan_0c                -0.0190  0.0505 20000  -0.1169    0.0801
## groupfu~~muskeltraint_0c          -1.3377  0.9500 20000  -3.2297    0.5090
## coplan_0c~~muskeltraint_0c         8.2658  3.0061 20000   2.3974   14.1869
## coplan_12c~~coplan_12c             1.8435  0.2072 20000   1.4387    2.2493
## muskeltraint_24~~muskeltraint_24 568.8286 61.6435 20000 449.6382  690.7300
## groupfu~~groupfu                   0.2499  0.0228 20000   0.2055    0.2943
## coplan_0c~~coplan_0c               2.4828  0.2271 20000   2.0278    2.9238
## muskeltraint_0c~~muskeltraint_0c 850.7023 77.2490 20000 698.5036 1000.1184
## coplan_12c~1                      -0.0401  0.1471 20000  -0.3294    0.2457
## muskeltraint_24~1                 21.7994  2.5624 20000  16.7869   26.8368
## groupfu~1                          0.5104  0.0323 20000   0.4459    0.5733
## coplan_0c~1                        0.0005  0.1020 20000  -0.1997    0.1984
## muskeltraint_0c~1                  0.0000  1.8749 20000  -3.7326    3.6424
## ind                                0.0152  0.3322 20000  -0.6552    0.7846
## total                              2.7859  3.6654 20000  -4.3334   10.0521

Coping Planning at 18 Months

model <- '
# Direct Effects
coplan_18c ~ a1*groupfu + a2*coplan_0c + a3*muskeltraint_0c
muskeltraint_24 ~ c1*groupfu + c2*coplan_0c + c3*muskeltraint_0c + b1*coplan_18c

# Covariances
groupfu ~~ coplan_0c + muskeltraint_0c 
coplan_0c ~~ muskeltraint_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 72 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         5
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   coplan_18c ~                                                           
##     groupfu   (a1)     0.213    0.228    0.938    0.348   -0.232    0.659
##     coplan_0c (a2)     0.343    0.073    4.717    0.000    0.200    0.485
##     mskltrn_0 (a3)    -0.002    0.005   -0.326    0.744   -0.012    0.009
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     2.827    3.689    0.766    0.444   -4.403   10.056
##     coplan_0c (c2)     3.163    1.310    2.415    0.016    0.595    5.730
##     mskltrn_0 (c3)     0.476    0.076    6.266    0.000    0.327    0.625
##     copln_18c (b1)    -0.248    1.499   -0.165    0.869   -3.186    2.691
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     coplan_0c        -0.018    0.051   -0.353    0.724   -0.118    0.082
##     muskeltrant_0c   -1.338    0.943   -1.418    0.156   -3.186    0.511
##   coplan_0c ~~                                                          
##     muskeltrant_0c    8.267    3.009    2.748    0.006    2.371   14.164
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_18c       -0.124    0.156   -0.794    0.427   -0.430    0.182
##    .muskeltrant_24   21.784    2.567    8.487    0.000   16.753   26.815
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     coplan_0c        -0.002    0.102   -0.015    0.988   -0.201    0.198
##     muskeltrant_0c    0.000    1.879    0.000    1.000   -3.682    3.682
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_18c        1.905    0.221    8.631    0.000    1.473    2.338
##    .muskeltrant_24  568.396   61.483    9.245    0.000  447.891  688.902
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     coplan_0c         2.484    0.227   10.960    0.000    2.040    2.928
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.053    0.322   -0.164    0.869   -0.683    0.577
##     total             2.774    3.668    0.756    0.450   -4.416    9.963

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                 0.2135  0.2252 20000  -0.2188    0.6596
## a2                                 0.3428  0.0731 20000   0.1977    0.4835
## a3                                -0.0017  0.0053 20000  -0.0122    0.0086
## c1                                 2.8265  3.6755 20000  -4.3021   10.1448
## c2                                 3.1625  1.3032 20000   0.5578    5.6887
## c3                                 0.4758  0.0760 20000   0.3292    0.6257
## b1                                -0.2476  1.4910 20000  -3.1923    2.6738
## groupfu~~coplan_0c                -0.0180  0.0506 20000  -0.1177    0.0798
## groupfu~~muskeltraint_0c          -1.3377  0.9500 20000  -3.2320    0.5085
## coplan_0c~~muskeltraint_0c         8.2674  3.0066 20000   2.3984   14.1897
## coplan_18c~~coplan_18c             1.9053  0.2213 20000   1.4731    2.3547
## muskeltraint_24~~muskeltraint_24 568.3963 61.5995 20000 449.2906  690.2109
## groupfu~~groupfu                   0.2499  0.0229 20000   0.2056    0.2943
## coplan_0c~~coplan_0c               2.4838  0.2289 20000   2.0335    2.9284
## muskeltraint_0c~~muskeltraint_0c 850.7023 77.2490 20000 698.5035 1000.1183
## coplan_18c~1                      -0.1239  0.1554 20000  -0.4321    0.1786
## muskeltraint_24~1                 21.7841  2.5724 20000  16.7750   26.8390
## groupfu~1                          0.5104  0.0324 20000   0.4478    0.5748
## coplan_0c~1                       -0.0016  0.1022 20000  -0.2010    0.1993
## muskeltraint_0c~1                  0.0000  1.8749 20000  -3.7322    3.6418
## ind                               -0.0529  0.4622 20000  -1.0749    0.9347
## total                              2.7737  3.6682 20000  -4.3198   10.0723

Maintenance Self-Efficacy at 12 Months

model <- '
# Direct Effects
aufswe_12c ~ a1*groupfu + a2*aufswe_0c + a3*muskeltraint_0c
muskeltraint_24 ~ c1*groupfu + c2*aufswe_0c + c3*muskeltraint_0c + b1*aufswe_12c

# Covariances
groupfu ~~ aufswe_0c + muskeltraint_0c 
aufswe_0c ~~ muskeltraint_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 47 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         4
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   aufswe_12c ~                                                           
##     groupfu   (a1)    -0.174    0.159   -1.096    0.273   -0.486    0.137
##     aufswe_0c (a2)     0.137    0.069    1.986    0.047    0.002    0.272
##     mskltrn_0 (a3)    -0.001    0.004   -0.380    0.704   -0.008    0.006
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     3.567    3.721    0.959    0.338   -3.725   10.860
##     aufswe_0c (c2)     2.792    1.610    1.734    0.083   -0.363    5.946
##     mskltrn_0 (c3)     0.500    0.076    6.592    0.000    0.351    0.648
##     aufsw_12c (b1)     0.827    1.940    0.426    0.670   -2.976    4.630
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     aufswe_0c        -0.066    0.038   -1.767    0.077   -0.140    0.007
##     muskeltrant_0c   -1.338    0.943   -1.418    0.156   -3.186    0.511
##   aufswe_0c ~~                                                          
##     muskeltrant_0c    4.183    2.197    1.903    0.057   -0.124    8.490
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_12c        0.087    0.108    0.807    0.419   -0.125    0.299
##    .muskeltrant_24   21.463    2.585    8.302    0.000   16.396   26.530
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     aufswe_0c        -0.000    0.075   -0.000    1.000   -0.147    0.147
##     muskeltrant_0c    0.000    1.879    0.000    1.000   -3.682    3.682
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_12c        1.001    0.111    9.000    0.000    0.783    1.219
##    .muskeltrant_24  578.292   62.550    9.245    0.000  455.697  700.888
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     aufswe_0c         1.347    0.123   10.977    0.000    1.107    1.588
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.144    0.359   -0.401    0.688   -0.848    0.560
##     total             3.423    3.705    0.924    0.356   -3.838   10.685

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                -0.1743  0.1576 20000  -0.4794    0.1394
## a2                                 0.1367  0.0693 20000   0.0005    0.2708
## a3                                -0.0014  0.0036 20000  -0.0085    0.0057
## c1                                 3.5672  3.7256 20000  -3.6726   10.8595
## c2                                 2.7915  1.6017 20000  -0.3688    5.8609
## c3                                 0.4997  0.0761 20000   0.3517    0.6484
## b1                                 0.8272  1.9364 20000  -2.9908    4.6420
## groupfu~~aufswe_0c                -0.0665  0.0374 20000  -0.1402    0.0055
## groupfu~~muskeltraint_0c          -1.3377  0.9496 20000  -3.1853    0.5537
## aufswe_0c~~muskeltraint_0c         4.1827  2.1974 20000  -0.1281    8.4639
## aufswe_12c~~aufswe_12c             1.0011  0.1121 20000   0.7820    1.2208
## muskeltraint_24~~muskeltraint_24 578.2921 62.6679 20000 454.3649  699.4637
## groupfu~~groupfu                   0.2499  0.0229 20000   0.2052    0.2943
## aufswe_0c~~aufswe_0c               1.3474  0.1231 20000   1.1085    1.5935
## muskeltraint_0c~~muskeltraint_0c 850.7023 77.2489 20000 701.2828 1002.8944
## aufswe_12c~1                       0.0872  0.1074 20000  -0.1238    0.2942
## muskeltraint_24~1                 21.4627  2.5638 20000  16.4896   26.5778
## groupfu~1                          0.5104  0.0324 20000   0.4458    0.5727
## aufswe_0c~1                        0.0000  0.0752 20000  -0.1491    0.1468
## muskeltraint_0c~1                  0.0000  1.8699 20000  -3.6522    3.6564
## ind                               -0.1442  0.4685 20000  -1.2338    0.7579
## total                              3.4230  3.7230 20000  -3.8461   10.7149

Maintenance Self-Efficacy at 18 Months

model <- '
# Direct Effects
aufswe_18c ~ a1*groupfu + a2*aufswe_0c + a3*muskeltraint_0c
muskeltraint_24 ~ c1*groupfu + c2*aufswe_0c + c3*muskeltraint_0c + b1*aufswe_18c

# Covariances
groupfu ~~ aufswe_0c + muskeltraint_0c 
aufswe_0c ~~ muskeltraint_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 68 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         4
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   aufswe_18c ~                                                           
##     groupfu   (a1)    -0.006    0.171   -0.036    0.972   -0.341    0.329
##     aufswe_0c (a2)     0.254    0.073    3.488    0.000    0.111    0.397
##     mskltrn_0 (a3)     0.005    0.004    1.227    0.220   -0.003    0.013
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     3.409    3.705    0.920    0.357   -3.852   10.670
##     aufswe_0c (c2)     2.842    1.657    1.715    0.086   -0.406    6.090
##     mskltrn_0 (c3)     0.496    0.076    6.511    0.000    0.347    0.645
##     aufsw_18c (b1)     0.302    1.900    0.159    0.874   -3.422    4.025
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     aufswe_0c        -0.066    0.038   -1.767    0.077   -0.140    0.007
##     muskeltrant_0c   -1.338    0.943   -1.418    0.156   -3.186    0.511
##   aufswe_0c ~~                                                          
##     muskeltrant_0c    4.183    2.197    1.903    0.057   -0.124    8.490
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_18c        0.036    0.117    0.304    0.761   -0.194    0.266
##    .muskeltrant_24   21.534    2.581    8.344    0.000   16.475   26.592
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     aufswe_0c        -0.000    0.075   -0.000    1.000   -0.147    0.147
##     muskeltrant_0c   -0.000    1.879   -0.000    1.000   -3.682    3.682
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_18c        1.093    0.125    8.718    0.000    0.848    1.339
##    .muskeltrant_24  578.878   62.606    9.246    0.000  456.172  701.585
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     aufswe_0c         1.347    0.123   10.977    0.000    1.107    1.588
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.002    0.052   -0.035    0.972   -0.104    0.100
##     total             3.408    3.705    0.920    0.358   -3.854   10.669

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                -0.0061  0.1695 20000  -0.3359    0.3332
## a2                                 0.2543  0.0730 20000   0.1123    0.3986
## a3                                 0.0049  0.0040 20000  -0.0029    0.0127
## c1                                 3.4093  3.6925 20000  -3.7963   10.7601
## c2                                 2.8420  1.6549 20000  -0.4331    6.0733
## c3                                 0.4959  0.0766 20000   0.3443    0.6445
## b1                                 0.3019  1.8921 20000  -3.4314    3.9822
## groupfu~~aufswe_0c                -0.0665  0.0374 20000  -0.1402    0.0060
## groupfu~~muskeltraint_0c          -1.3377  0.9496 20000  -3.2287    0.5105
## aufswe_0c~~muskeltraint_0c         4.1827  2.1974 20000  -0.0984    8.4929
## aufswe_18c~~aufswe_18c             1.0934  0.1257 20000   0.8379    1.3389
## muskeltraint_24~~muskeltraint_24 578.8781 62.7246 20000 454.8385  700.1589
## groupfu~~groupfu                   0.2499  0.0229 20000   0.2054    0.2945
## aufswe_0c~~aufswe_0c               1.3474  0.1239 20000   1.1035    1.5891
## muskeltraint_0c~~muskeltraint_0c 850.7023 77.2489 20000 698.5103 1000.1218
## aufswe_18c~1                       0.0357  0.1165 20000  -0.1957    0.2605
## muskeltraint_24~1                 21.5335  2.5832 20000  16.5246   26.6203
## groupfu~1                          0.5104  0.0324 20000   0.4475    0.5748
## aufswe_0c~1                        0.0000  0.0751 20000  -0.1473    0.1474
## muskeltraint_0c~1                  0.0000  1.8699 20000  -3.6526    3.6571
## ind                               -0.0018  0.3286 20000  -0.6547    0.7554
## total                              3.4075  3.7045 20000  -3.7845   10.7614

Recovery Self-Efficacy at 12 Months

model <- '
# Direct Effects
wieswe_12c ~ a1*groupfu + a2*wieswe_0c + a3*muskeltraint_0c
muskeltraint_24 ~ c1*groupfu + c2*wieswe_0c + c3*muskeltraint_0c + b1*wieswe_12c

# Covariances
groupfu ~~ wieswe_0c + muskeltraint_0c 
wieswe_0c ~~ muskeltraint_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 63 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         5
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wieswe_12c ~                                                           
##     groupfu   (a1)     0.207    0.148    1.404    0.160   -0.082    0.497
##     wieswe_0c (a2)     0.269    0.072    3.717    0.000    0.127    0.411
##     mskltrn_0 (a3)    -0.004    0.003   -1.087    0.277   -0.010    0.003
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     3.660    3.714    0.986    0.324   -3.619   10.938
##     wieswe_0c (c2)     3.936    1.875    2.099    0.036    0.260    7.612
##     mskltrn_0 (c3)     0.498    0.076    6.587    0.000    0.350    0.646
##     wiesw_12c (b1)    -0.596    2.075   -0.287    0.774   -4.663    3.471
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     wieswe_0c        -0.062    0.033   -1.894    0.058   -0.126    0.002
##     muskeltrant_0c   -1.338    0.943   -1.418    0.156   -3.186    0.511
##   wieswe_0c ~~                                                          
##     muskeltrant_0c    2.841    1.893    1.501    0.133   -0.870    6.552
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_12c       -0.088    0.100   -0.877    0.381   -0.285    0.109
##    .muskeltrant_24   21.456    2.580    8.318    0.000   16.401   26.512
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     wieswe_0c         0.000    0.065    0.007    0.995   -0.126    0.127
##     muskeltrant_0c    0.000    1.879    0.000    1.000   -3.682    3.682
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_12c        0.864    0.096    9.000    0.000    0.676    1.052
##    .muskeltrant_24  575.249   62.216    9.246    0.000  453.308  697.190
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     wieswe_0c         1.006    0.092   10.955    0.000    0.826    1.186
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.124    0.437   -0.283    0.777   -0.980    0.733
##     total             3.536    3.697    0.956    0.339   -3.710   10.783

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                 0.2075  0.1461 20000  -0.0733    0.4981
## a2                                 0.2692  0.0729 20000   0.1241    0.4119
## a3                                -0.0036  0.0033 20000  -0.0102    0.0029
## c1                                 3.6598  3.7018 20000  -3.6412   10.9365
## c2                                 3.9358  1.8648 20000   0.2715    7.5595
## c3                                 0.4976  0.0761 20000   0.3476    0.6458
## b1                                -0.5960  2.0702 20000  -4.6294    3.4882
## groupfu~~wieswe_0c                -0.0618  0.0325 20000  -0.1257    0.0013
## groupfu~~muskeltraint_0c          -1.3377  0.9523 20000  -3.2028    0.5206
## wieswe_0c~~muskeltraint_0c         2.8413  1.8899 20000  -0.8949    6.5087
## wieswe_12c~~wieswe_12c             0.8637  0.0962 20000   0.6759    1.0591
## muskeltraint_24~~muskeltraint_24 575.2489 62.3333 20000 454.7244  698.5149
## groupfu~~groupfu                   0.2499  0.0228 20000   0.2050    0.2943
## wieswe_0c~~wieswe_0c               1.0060  0.0924 20000   0.8253    1.1866
## muskeltraint_0c~~muskeltraint_0c 850.7023 77.2488 20000 701.2828 1002.8942
## wieswe_12c~1                      -0.0880  0.0999 20000  -0.2863    0.1063
## muskeltraint_24~1                 21.4564  2.5900 20000  16.3568   26.4918
## groupfu~1                          0.5104  0.0323 20000   0.4476    0.5748
## wieswe_0c~1                        0.0004  0.0652 20000  -0.1261    0.1303
## muskeltraint_0c~1                  0.0000  1.8699 20000  -3.6525    3.6568
## ind                               -0.1237  0.5307 20000  -1.3343    0.9466
## total                              3.5362  3.6977 20000  -3.7174   10.7829

Recovery Self-Efficacy at 18 Months

model <- '
# Direct Effects
wieswe_18c ~ a1*groupfu + a2*wieswe_0c + a3*muskeltraint_0c
muskeltraint_24 ~ c1*groupfu + c2*wieswe_0c + c3*muskeltraint_0c + b1*wieswe_18c

# Covariances
groupfu ~~ wieswe_0c + muskeltraint_0c 
wieswe_0c ~~ muskeltraint_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 67 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         5
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wieswe_18c ~                                                           
##     groupfu   (a1)    -0.059    0.166   -0.357    0.721   -0.384    0.266
##     wieswe_0c (a2)     0.300    0.083    3.605    0.000    0.137    0.464
##     mskltrn_0 (a3)     0.004    0.004    1.073    0.283   -0.004    0.013
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     3.615    3.701    0.977    0.329   -3.638   10.868
##     wieswe_0c (c2)     3.597    1.882    1.911    0.056   -0.093    7.286
##     mskltrn_0 (c3)     0.498    0.075    6.598    0.000    0.350    0.646
##     wiesw_18c (b1)     0.604    1.987    0.304    0.761   -3.290    4.498
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     wieswe_0c        -0.062    0.033   -1.894    0.058   -0.126    0.002
##     muskeltrant_0c   -1.338    0.943   -1.418    0.156   -3.186    0.511
##   wieswe_0c ~~                                                          
##     muskeltrant_0c    2.841    1.893    1.501    0.133   -0.870    6.552
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_18c        0.062    0.114    0.543    0.587   -0.162    0.286
##    .muskeltrant_24   21.467    2.576    8.332    0.000   16.418   26.517
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     wieswe_0c         0.000    0.065    0.007    0.995   -0.126    0.127
##     muskeltrant_0c   -0.000    1.879   -0.000    1.000   -3.682    3.682
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_18c        1.018    0.118    8.660    0.000    0.788    1.248
##    .muskeltrant_24  575.180   62.213    9.245    0.000  453.244  697.117
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     wieswe_0c         1.006    0.092   10.955    0.000    0.826    1.186
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.036    0.149   -0.239    0.811   -0.328    0.257
##     total             3.579    3.697    0.968    0.333   -3.668   10.826

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                -0.0591  0.1642 20000  -0.3755    0.2648
## a2                                 0.3004  0.0837 20000   0.1363    0.4636
## a3                                 0.0044  0.0042 20000  -0.0038    0.0126
## c1                                 3.6150  3.6874 20000  -3.6076   10.8722
## c2                                 3.5968  1.8730 20000  -0.0795    7.2998
## c3                                 0.4977  0.0755 20000   0.3475    0.6448
## b1                                 0.6040  1.9842 20000  -3.2884    4.4722
## groupfu~~wieswe_0c                -0.0618  0.0325 20000  -0.1246    0.0021
## groupfu~~muskeltraint_0c          -1.3377  0.9483 20000  -3.2365    0.5012
## wieswe_0c~~muskeltraint_0c         2.8413  1.8916 20000  -0.8430    6.5832
## wieswe_18c~~wieswe_18c             1.0180  0.1178 20000   0.7783    1.2482
## muskeltraint_24~~muskeltraint_24 575.1804 62.3309 20000 454.6605  698.4419
## groupfu~~groupfu                   0.2499  0.0228 20000   0.2055    0.2946
## wieswe_0c~~wieswe_0c               1.0060  0.0924 20000   0.8252    1.1864
## muskeltraint_0c~~muskeltraint_0c 850.7023 77.2488 20000 698.5077 1000.1255
## wieswe_18c~1                       0.0620  0.1137 20000  -0.1616    0.2822
## muskeltraint_24~1                 21.4675  2.5865 20000  16.4008   26.4982
## groupfu~1                          0.5104  0.0323 20000   0.4459    0.5730
## wieswe_0c~1                        0.0004  0.0651 20000  -0.1277    0.1262
## muskeltraint_0c~1                  0.0000  1.8699 20000  -3.6571    3.6523
## ind                               -0.0357  0.3572 20000  -0.8075    0.7495
## total                              3.5793  3.6990 20000  -3.6245   10.8760

Action Control at 12 Months

model <- '
# Direct Effects
hk_12c ~ a1*groupfu + a2*hk_0c + a3*muskeltraint_0c
muskeltraint_24 ~ c1*groupfu + c2*hk_0c + c3*muskeltraint_0c + b1*hk_12c

# Covariances
groupfu ~~ hk_0c + muskeltraint_0c 
hk_0c ~~ muskeltraint_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 70 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         4
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   hk_12c ~                                                               
##     groupfu   (a1)     0.355    0.180    1.971    0.049    0.002    0.708
##     hk_0c     (a2)     0.396    0.064    6.208    0.000    0.271    0.521
##     mskltrn_0 (a3)     0.002    0.004    0.509    0.611   -0.006    0.010
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     2.865    3.718    0.770    0.441   -4.423   10.153
##     hk_0c     (c2)     3.097    1.449    2.138    0.033    0.257    5.937
##     mskltrn_0 (c3)     0.496    0.075    6.651    0.000    0.350    0.642
##     hk_12c    (b1)     0.485    1.715    0.283    0.777   -2.876    3.846
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     hk_0c            -0.040    0.046   -0.874    0.382   -0.130    0.050
##     muskeltrant_0c   -1.338    0.943   -1.418    0.156   -3.186    0.511
##   hk_0c ~~                                                              
##     muskeltrant_0c    7.040    2.705    2.602    0.009    1.738   12.342
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_12c           -0.178    0.123   -1.447    0.148   -0.420    0.063
##    .muskeltrant_24   21.547    2.573    8.373    0.000   16.503   26.591
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     hk_0c             0.000    0.091    0.000    1.000   -0.179    0.179
##     muskeltrant_0c    0.000    1.879    0.000    1.000   -3.682    3.682
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_12c            1.288    0.144    8.972    0.000    1.007    1.569
##    .muskeltrant_24  568.517   61.488    9.246    0.000  448.003  689.031
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     hk_0c             2.015    0.184   10.977    0.000    1.655    2.375
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.172    0.617    0.279    0.780   -1.038    1.382
##     total             3.037    3.666    0.828    0.407   -4.149   10.223

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                 0.3549  0.1782 20000   0.0118    0.7078
## a2                                 0.3961  0.0640 20000   0.2696    0.5199
## a3                                 0.0021  0.0041 20000  -0.0060    0.0101
## c1                                 2.8647  3.7252 20000  -4.4434   10.1213
## c2                                 3.0974  1.4391 20000   0.2824    5.8808
## c3                                 0.4957  0.0747 20000   0.3472    0.6408
## b1                                 0.4850  1.7111 20000  -2.8800    3.8210
## groupfu~~hk_0c                    -0.0400  0.0456 20000  -0.1296    0.0489
## groupfu~~muskeltraint_0c          -1.3377  0.9501 20000  -3.2312    0.5084
## hk_0c~~muskeltraint_0c             7.0401  2.7108 20000   1.6721   12.3301
## hk_12c~~hk_12c                     1.2880  0.1447 20000   1.0047    1.5708
## muskeltraint_24~~muskeltraint_24 568.5171 61.6039 20000 446.6937  687.6313
## groupfu~~groupfu                   0.2499  0.0228 20000   0.2056    0.2945
## hk_0c~~hk_0c                       2.0150  0.1840 20000   1.6554    2.3831
## muskeltraint_0c~~muskeltraint_0c 850.7023 77.2487 20000 698.5222 1000.0925
## hk_12c~1                          -0.1783  0.1229 20000  -0.4189    0.0605
## muskeltraint_24~1                 21.5473  2.5519 20000  16.4410   26.5281
## groupfu~1                          0.5104  0.0323 20000   0.4475    0.5745
## hk_0c~1                            0.0000  0.0920 20000  -0.1826    0.1796
## muskeltraint_0c~1                  0.0000  1.8699 20000  -3.6516    3.6560
## ind                                0.1721  0.6899 20000  -1.1654    1.7505
## total                              3.0369  3.6920 20000  -4.1242   10.2541

Action Control at 18 Months

model <- '
# Direct Effects
hk_18c ~ a1*groupfu + a2*hk_0c + a3*muskeltraint_0c
muskeltraint_24 ~ c1*groupfu + c2*hk_0c + c3*muskeltraint_0c + b1*hk_18c

# Covariances
groupfu ~~ hk_0c + muskeltraint_0c 
hk_0c ~~ muskeltraint_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 69 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         4
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   hk_18c ~                                                               
##     groupfu   (a1)     0.251    0.171    1.471    0.141   -0.084    0.587
##     hk_0c     (a2)     0.374    0.061    6.171    0.000    0.255    0.492
##     mskltrn_0 (a3)     0.003    0.004    0.689    0.491   -0.005    0.010
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     2.925    3.713    0.788    0.431   -4.352   10.202
##     hk_0c     (c2)     3.140    1.483    2.118    0.034    0.234    6.046
##     mskltrn_0 (c3)     0.495    0.075    6.627    0.000    0.349    0.642
##     hk_18c    (b1)     0.387    1.950    0.198    0.843   -3.435    4.208
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     hk_0c            -0.040    0.046   -0.874    0.382   -0.130    0.050
##     muskeltrant_0c   -1.338    0.943   -1.418    0.156   -3.186    0.511
##   hk_0c ~~                                                              
##     muskeltrant_0c    7.040    2.705    2.602    0.009    1.738   12.342
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_18c           -0.139    0.117   -1.183    0.237   -0.369    0.091
##    .muskeltrant_24   21.527    2.576    8.358    0.000   16.478   26.575
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     hk_0c             0.000    0.091    0.000    1.000   -0.179    0.179
##     muskeltrant_0c    0.000    1.879    0.000    1.000   -3.682    3.682
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_18c            1.091    0.126    8.689    0.000    0.845    1.337
##    .muskeltrant_24  568.658   61.503    9.246    0.000  448.115  689.201
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     hk_0c             2.015    0.184   10.977    0.000    1.655    2.375
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.097    0.498    0.195    0.845   -0.880    1.074
##     total             3.022    3.668    0.824    0.410   -4.166   10.210

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                 0.2514  0.1692 20000  -0.0743    0.5864
## a2                                 0.3735  0.0609 20000   0.2567    0.4941
## a3                                 0.0027  0.0040 20000  -0.0051    0.0105
## c1                                 2.9248  3.7209 20000  -4.3593   10.2005
## c2                                 3.1401  1.4718 20000   0.2457    5.9804
## c3                                 0.4952  0.0753 20000   0.3491    0.6449
## b1                                 0.3868  1.9448 20000  -3.4424    4.2063
## groupfu~~hk_0c                    -0.0400  0.0455 20000  -0.1299    0.0480
## groupfu~~muskeltraint_0c          -1.3377  0.9501 20000  -3.1839    0.5562
## hk_0c~~muskeltraint_0c             7.0401  2.7108 20000   1.7554   12.4125
## hk_18c~~hk_18c                     1.0912  0.1266 20000   0.8438    1.3390
## muskeltraint_24~~muskeltraint_24 568.6582 61.6187 20000 446.8056  687.8012
## groupfu~~groupfu                   0.2499  0.0228 20000   0.2054    0.2943
## hk_0c~~hk_0c                       2.0150  0.1840 20000   1.6563    2.3848
## muskeltraint_0c~~muskeltraint_0c 850.7023 77.2487 20000 701.3122 1002.8824
## hk_18c~1                          -0.1389  0.1169 20000  -0.3689    0.0902
## muskeltraint_24~1                 21.5265  2.5534 20000  16.4353   26.5090
## groupfu~1                          0.5104  0.0324 20000   0.4460    0.5728
## hk_0c~1                            0.0000  0.0919 20000  -0.1803    0.1808
## muskeltraint_0c~1                  0.0000  1.8699 20000  -3.6516    3.6582
## ind                                0.0972  0.5988 20000  -1.0537    1.5112
## total                              3.0220  3.6956 20000  -4.1536   10.2566

Collaborative Planning at 18 Months

model <- '
# Direct Effects
collimpint_18c ~ a1*groupfu + a2*collimpint_0c + a3*muskeltraint_0c
muskeltraint_24 ~ c1*groupfu + c2*collimpint_0c + c3*muskeltraint_0c + b1*collimpint_18c

# Covariances
groupfu ~~ collimpint_0c + muskeltraint_0c 
collimpint_0c ~~ muskeltraint_0c

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 95 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                        20
## 
##   Number of observations                           241
##   Number of missing patterns                         8
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   collimpint_18c ~                                                       
##     groupfu   (a1)     0.305    0.386    0.791    0.429   -0.451    1.061
##     cllmpnt_0 (a2)     0.480    0.096    5.018    0.000    0.292    0.667
##     mskltrn_0 (a3)    -0.020    0.010   -2.069    0.039   -0.039   -0.001
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     1.719    3.784    0.454    0.650   -5.698    9.135
##     cllmpnt_0 (c2)     0.931    1.433    0.649    0.516   -1.879    3.740
##     mskltrn_0 (c3)     0.532    0.083    6.421    0.000    0.370    0.695
##     cllmpn_18 (b1)     1.756    1.523    1.153    0.249   -1.230    4.741
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                            
##     collimpint_0c     0.013    0.091    0.145    0.885   -0.166    0.192
##     muskeltrant_0c   -1.338    0.943   -1.418    0.156   -3.186    0.511
##   collimpint_0c ~~                                                      
##     muskeltrant_0c    5.895    5.202    1.133    0.257   -4.301   16.090
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_18c   -0.212    0.246   -0.861    0.389   -0.695    0.271
##    .muskeltrant_24   22.560    2.625    8.595    0.000   17.416   27.705
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     collimpint_0c    -0.117    0.183   -0.641    0.522   -0.475    0.241
##     muskeltrant_0c   -0.000    1.879   -0.000    1.000   -3.682    3.682
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_18c    2.684    0.458    5.863    0.000    1.787    3.582
##    .muskeltrant_24  569.020   62.875    9.050    0.000  445.786  692.253
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     collimpint_0c     4.438    0.561    7.911    0.000    3.338    5.538
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.535    0.849    0.630    0.528   -1.129    2.199
##     total             2.254    3.760    0.600    0.549   -5.115    9.623

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                 0.3048  0.3889 20000  -0.4573    1.0741
## a2                                 0.4799  0.0958 20000   0.2897    0.6672
## a3                                -0.0200  0.0097 20000  -0.0390   -0.0008
## c1                                 1.7189  3.7870 20000  -5.6185    9.2028
## c2                                 0.9305  1.4315 20000  -1.8935    3.7386
## c3                                 0.5322  0.0823 20000   0.3723    0.6940
## b1                                 1.7559  1.5176 20000  -1.2189    4.7005
## groupfu~~collimpint_0c             0.0132  0.0918 20000  -0.1666    0.1925
## groupfu~~muskeltraint_0c          -1.3377  0.9395 20000  -3.1519    0.5424
## collimpint_0c~~muskeltraint_0c     5.8945  5.2034 20000  -4.2709   16.1448
## collimpint_18c~~collimpint_18c     2.6842  0.4582 20000   1.7740    3.5715
## muskeltraint_24~~muskeltraint_24 569.0200 62.9940 20000 444.4572  690.8181
## groupfu~~groupfu                   0.2499  0.0228 20000   0.2054    0.2943
## collimpint_0c~~collimpint_0c       4.4381  0.5551 20000   3.3349    5.5034
## muskeltraint_0c~~muskeltraint_0c 850.7024 77.2486 20000 701.2856 1002.9588
## collimpint_18c~1                  -0.2123  0.2469 20000  -0.6958    0.2711
## muskeltraint_24~1                 22.5604  2.6163 20000  17.3703   27.6546
## groupfu~1                          0.5104  0.0324 20000   0.4476    0.5745
## collimpint_0c~1                   -0.1172  0.1850 20000  -0.4801    0.2459
## muskeltraint_0c~1                  0.0000  1.8749 20000  -3.7304    3.6424
## ind                                0.5353  1.0350 20000  -1.0139    3.1787
## total                              2.2542  3.8039 20000  -5.1032    9.8423

S-Table 9

Action Planning at 12 Months

model <- '
# Direct Effects
acplan_12c ~ a1*groupfu + a2*acplan_0c + a3*muskeltraint_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
muskeltraint_24 ~ c1*groupfu + c2*acplan_0c + c3*muskeltraint_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*acplan_12c

# Covariances
groupfu ~~ acplan_0c + muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
acplan_0c ~~ muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
muskeltraint_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 422 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        11
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   acplan_12c ~                                                           
##     groupfu   (a1)     0.450    0.243    1.853    0.064   -0.026    0.925
##     acpln_0c  (a2)     0.370    0.067    5.499    0.000    0.238    0.502
##     mskltr_0  (a3)    -0.001    0.006   -0.139    0.889   -0.012    0.010
##     HIE       (a4)     0.266    0.292    0.908    0.364   -0.308    0.839
##     LIE       (a5)     0.384    0.295    1.304    0.192   -0.193    0.961
##     sex_0     (a6)     0.328    0.247    1.326    0.185   -0.157    0.812
##     age_0c    (a7)     0.031    0.017    1.810    0.070   -0.003    0.064
##     bmi_0c    (a8)    -0.011    0.026   -0.443    0.658   -0.062    0.039
##     panvs_0c  (a9)    -0.031    0.065   -0.480    0.632   -0.160    0.097
##     poshe_0c (a10)    -0.024    0.144   -0.171    0.865   -0.306    0.257
##     swsrNA_0 (a11)    -0.197    0.121   -1.628    0.104   -0.434    0.040
##     fam3_0   (a12)     0.155    0.319    0.486    0.627   -0.470    0.780
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     4.517    3.577    1.263    0.207   -2.494   11.529
##     acpln_0c  (c2)     2.750    1.103    2.493    0.013    0.588    4.911
##     mskltr_0  (c3)     0.445    0.074    6.036    0.000    0.300    0.589
##     HIE       (c4)    -4.314    4.298   -1.004    0.315  -12.738    4.109
##     LIE       (c5)     1.392    4.369    0.319    0.750   -7.171    9.955
##     sex_0     (c6)    -1.982    3.653   -0.543    0.587   -9.141    5.177
##     age_0c    (c7)     0.942    0.256    3.684    0.000    0.441    1.443
##     bmi_0c    (c8)    -0.104    0.383   -0.271    0.786   -0.854    0.647
##     panvs_0c  (c9)     2.048    0.951    2.154    0.031    0.184    3.913
##     poshe_0c (c10)     2.275    2.069    1.099    0.272   -1.780    6.330
##     swsrNA_0 (c11)    -2.984    1.711   -1.744    0.081   -6.339    0.370
##     fam3_0   (c12)    -3.043    4.750   -0.641    0.522  -12.353    6.267
##     acpln_12  (b1)    -0.903    1.219   -0.741    0.459   -3.292    1.486
## 
## Covariances:
##                      Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                              
##     acplan_0c          -0.007    0.057   -0.132    0.895   -0.119    0.104
##     muskeltrant_0c     -1.338    0.943   -1.418    0.156   -3.186    0.511
##     HIE                 0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE                -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0               0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c             -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c             -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c         -0.154    0.066   -2.353    0.019   -0.283   -0.026
##     poshee_0c           0.003    0.028    0.104    0.918   -0.052    0.058
##     swesourceNA_0c      0.069    0.038    1.806    0.071   -0.006    0.145
##     fam3_0             -0.033    0.013   -2.607    0.009   -0.057   -0.008
##   acplan_0c ~~                                                            
##     muskeltrant_0c      7.249    3.340    2.170    0.030    0.703   13.796
##     HIE                 0.038    0.054    0.717    0.473   -0.067    0.144
##     LIE                 0.000    0.054    0.004    0.997   -0.105    0.105
##     sex_0              -0.013    0.055   -0.244    0.807   -0.121    0.094
##     age_0c             -0.075    0.862   -0.088    0.930   -1.764    1.613
##     bmi_0c             -1.099    0.556   -1.978    0.048   -2.189   -0.010
##     painvas_0c          0.051    0.228    0.223    0.824   -0.397    0.498
##     poshee_0c           0.254    0.101    2.523    0.012    0.057    0.451
##     swesourceNA_0c     -0.149    0.136   -1.101    0.271   -0.415    0.116
##     fam3_0              0.037    0.044    0.841    0.400   -0.049    0.122
##   muskeltraint_0c ~~                                                      
##     HIE                 0.537    0.888    0.605    0.545   -1.204    2.278
##     LIE                -0.258    0.890   -0.289    0.772   -2.003    1.487
##     sex_0              -0.916    0.911   -1.005    0.315   -2.701    0.869
##     age_0c             11.641   14.295    0.814    0.415  -16.377   39.659
##     bmi_0c            -24.200    9.267   -2.612    0.009  -42.362   -6.038
##     painvas_0c         -1.207    3.767   -0.320    0.749   -8.590    6.176
##     poshee_0c           2.153    1.640    1.313    0.189   -1.062    5.369
##     swesourceNA_0c     -2.637    2.218   -1.189    0.234   -6.984    1.710
##     fam3_0              1.073    0.724    1.482    0.138   -0.346    2.493
##   HIE ~~                                                                  
##     LIE                -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0              -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c              0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c              0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c         -0.023    0.061   -0.378    0.705   -0.143    0.097
##     poshee_0c           0.044    0.027    1.637    0.102   -0.009    0.096
##     swesourceNA_0c     -0.034    0.036   -0.940    0.347   -0.105    0.037
##     fam3_0              0.002    0.012    0.180    0.857   -0.021    0.025
##   LIE ~~                                                                  
##     sex_0               0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c             -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c             -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c          0.035    0.062    0.573    0.567   -0.086    0.156
##     poshee_0c          -0.040    0.027   -1.482    0.138   -0.092    0.013
##     swesourceNA_0c      0.008    0.036    0.229    0.819   -0.063    0.079
##     fam3_0             -0.006    0.012   -0.547    0.585   -0.029    0.017
##   sex_0 ~~                                                                
##     age_0c              0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c             -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c         -0.006    0.063   -0.095    0.924   -0.129    0.117
##     poshee_0c          -0.033    0.027   -1.230    0.219   -0.087    0.020
##     swesourceNA_0c     -0.076    0.037   -2.039    0.041   -0.149   -0.003
##     fam3_0             -0.025    0.012   -2.098    0.036   -0.049   -0.002
##   age_0c ~~                                                               
##     bmi_0c             -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c         -2.771    0.997   -2.780    0.005   -4.725   -0.817
##     poshee_0c          -1.304    0.434   -3.003    0.003   -2.154   -0.453
##     swesourceNA_0c     -1.088    0.581   -1.872    0.061   -2.226    0.051
##     fam3_0             -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                               
##     painvas_0c          1.828    0.643    2.842    0.004    0.567    3.088
##     poshee_0c           0.064    0.274    0.232    0.817   -0.474    0.601
##     swesourceNA_0c      0.665    0.375    1.773    0.076   -0.070    1.400
##     fam3_0              0.055    0.121    0.460    0.646   -0.181    0.292
##   painvas_0c ~~                                                           
##     poshee_0c          -0.022    0.120   -0.182    0.855   -0.258    0.214
##     swesourceNA_0c      0.354    0.154    2.306    0.021    0.053    0.655
##     fam3_0              0.062    0.051    1.210    0.226   -0.038    0.161
##   poshee_0c ~~                                                            
##     swesourceNA_0c     -0.124    0.067   -1.858    0.063   -0.255    0.007
##     fam3_0              0.003    0.022    0.133    0.894   -0.039    0.045
##   swesourceNA_0c ~~                                                       
##     fam3_0              0.006    0.029    0.199    0.842   -0.052    0.063
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_12c       -0.611    0.256   -2.389    0.017   -1.111   -0.110
##    .muskeltrant_24   22.691    3.899    5.820    0.000   15.050   30.333
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     acplan_0c        -0.000    0.113   -0.000    1.000   -0.222    0.222
##     muskeltrant_0c   -0.000    1.879   -0.000    1.000   -3.682    3.682
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c            0.000    0.489    0.000    1.000   -0.959    0.959
##     bmi_0c            0.000    0.313    0.000    1.000   -0.614    0.614
##     painvas_0c       -0.012    0.130   -0.092    0.927   -0.266    0.242
##     poshee_0c        -0.000    0.056   -0.000    1.000   -0.110    0.110
##     swesourceNA_0c   -0.001    0.076   -0.019    0.985   -0.151    0.148
##     fam3_0            0.179    0.025    7.230    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_12c        2.168    0.243    8.910    0.000    1.691    2.645
##    .muskeltrant_24  503.076   54.463    9.237    0.000  396.330  609.822
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     acplan_0c         3.099    0.282   10.977    0.000    2.545    3.652
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.973    0.366   10.843    0.000    3.255    4.691
##     poshee_0c         0.757    0.069   10.959    0.000    0.622    0.892
##     swesourceNA_0c    1.350    0.126   10.739    0.000    1.104    1.597
##     fam3_0            0.147    0.013   10.956    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.406    0.580   -0.700    0.484   -1.543    0.731
##     total             4.111    3.549    1.159    0.247   -2.844   11.066

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                 0.4496  0.2432 20000  -0.0215    0.9237
## a2                                 0.3698  0.0672 20000   0.2386    0.5013
## a3                                -0.0008  0.0056 20000  -0.0116    0.0103
## a4                                 0.2656  0.2924 20000  -0.3026    0.8384
## a5                                 0.3840  0.2947 20000  -0.1998    0.9535
## a6                                 0.3276  0.2481 20000  -0.1603    0.8113
## a7                                 0.0309  0.0170 20000  -0.0022    0.0643
## a8                                -0.0115  0.0258 20000  -0.0617    0.0395
## a9                                -0.0314  0.0655 20000  -0.1603    0.0968
## a10                               -0.0245  0.1431 20000  -0.3059    0.2592
## a11                               -0.1968  0.1210 20000  -0.4359    0.0413
## a12                                0.1550  0.3184 20000  -0.4714    0.7805
## c1                                 4.5172  3.5365 20000  -2.5464   11.3985
## c2                                 2.7496  1.1043 20000   0.5743    4.9096
## c3                                 0.4447  0.0740 20000   0.2979    0.5889
## c4                                -4.3142  4.2883 20000 -12.6558    4.2131
## c5                                 1.3921  4.3572 20000  -6.9720   10.0633
## c6                                -1.9818  3.6429 20000  -8.9743    5.2280
## c7                                 0.9422  0.2564 20000   0.4452    1.4465
## c8                                -0.1039  0.3827 20000  -0.8476    0.6525
## c9                                 2.0485  0.9503 20000   0.1704    3.9322
## c10                                2.2748  2.0793 20000  -1.7767    6.3171
## c11                               -2.9841  1.7213 20000  -6.4316    0.3464
## c12                               -3.0429  4.7022 20000 -12.0463    6.2427
## b1                                -0.9028  1.2223 20000  -3.2875    1.4973
## groupfu~~acplan_0c                -0.0075  0.0565 20000  -0.1159    0.1048
## groupfu~~muskeltraint_0c          -1.3377  0.9500 20000  -3.1916    0.5135
## groupfu~~HIE                       0.0069  0.0152 20000  -0.0232    0.0365
## groupfu~~LIE                      -0.0035  0.0152 20000  -0.0331    0.0264
## groupfu~~sex_0                     0.0003  0.0156 20000  -0.0302    0.0310
## groupfu~~age_0c                   -0.0706  0.2457 20000  -0.5536    0.4126
## groupfu~~bmi_0c                   -0.0398  0.1557 20000  -0.3474    0.2651
## groupfu~~painvas_0c               -0.1543  0.0653 20000  -0.2821   -0.0261
## groupfu~~poshee_0c                 0.0029  0.0280 20000  -0.0523    0.0581
## groupfu~~swesourceNA_0c            0.0695  0.0384 20000  -0.0055    0.1445
## groupfu~~fam3_0                   -0.0327  0.0125 20000  -0.0575   -0.0083
## acplan_0c~~muskeltraint_0c         7.2492  3.3479 20000   0.4879   13.7221
## acplan_0c~~HIE                     0.0385  0.0539 20000  -0.0661    0.1446
## acplan_0c~~LIE                     0.0002  0.0539 20000  -0.1071    0.1050
## acplan_0c~~sex_0                  -0.0134  0.0552 20000  -0.1198    0.0967
## acplan_0c~~age_0c                 -0.0755  0.8641 20000  -1.7561    1.6260
## acplan_0c~~bmi_0c                 -1.0993  0.5532 20000  -2.1768   -0.0122
## acplan_0c~~painvas_0c              0.0509  0.2305 20000  -0.3958    0.5038
## acplan_0c~~poshee_0c               0.2536  0.0997 20000   0.0562    0.4481
## acplan_0c~~swesourceNA_0c         -0.1492  0.1367 20000  -0.4213    0.1125
## acplan_0c~~fam3_0                  0.0367  0.0438 20000  -0.0482    0.1223
## muskeltraint_0c~~HIE               0.5372  0.8933 20000  -1.2189    2.2842
## muskeltraint_0c~~LIE              -0.2575  0.8916 20000  -2.0168    1.4704
## muskeltraint_0c~~sex_0            -0.9157  0.9118 20000  -2.6931    0.8625
## muskeltraint_0c~~age_0c           11.6412 14.2885 20000 -16.5717   39.7194
## muskeltraint_0c~~bmi_0c          -24.2002  9.2825 20000 -42.2486   -5.9399
## muskeltraint_0c~~painvas_0c       -1.2073  3.7933 20000  -8.7185    6.1346
## muskeltraint_0c~~poshee_0c         2.1533  1.6444 20000  -1.0861    5.3373
## muskeltraint_0c~~swesourceNA_0c   -2.6370  2.2211 20000  -6.9726    1.7113
## muskeltraint_0c~~fam3_0            1.0734  0.7200 20000  -0.3386    2.4702
## HIE~~LIE                          -0.1144  0.0162 20000  -0.1460   -0.0826
## HIE~~sex_0                        -0.0010  0.0149 20000  -0.0303    0.0283
## HIE~~age_0c                        0.1878  0.2328 20000  -0.2711    0.6431
## HIE~~bmi_0c                        0.3432  0.1504 20000   0.0509    0.6388
## HIE~~painvas_0c                   -0.0231  0.0611 20000  -0.1438    0.0956
## HIE~~poshee_0c                     0.0438  0.0266 20000  -0.0085    0.0956
## HIE~~swesourceNA_0c               -0.0341  0.0362 20000  -0.1054    0.0356
## HIE~~fam3_0                        0.0021  0.0116 20000  -0.0206    0.0248
## LIE~~sex_0                         0.0016  0.0148 20000  -0.0276    0.0306
## LIE~~age_0c                       -0.2255  0.2312 20000  -0.6772    0.2243
## LIE~~bmi_0c                       -0.3705  0.1514 20000  -0.6705   -0.0731
## LIE~~painvas_0c                    0.0353  0.0617 20000  -0.0861    0.1565
## LIE~~poshee_0c                    -0.0396  0.0266 20000  -0.0918    0.0122
## LIE~~swesourceNA_0c                0.0083  0.0363 20000  -0.0624    0.0797
## LIE~~fam3_0                       -0.0064  0.0117 20000  -0.0295    0.0164
## sex_0~~age_0c                      0.0533  0.2376 20000  -0.4121    0.5190
## sex_0~~bmi_0c                     -0.1011  0.1518 20000  -0.3985    0.1970
## sex_0~~painvas_0c                 -0.0060  0.0633 20000  -0.1306    0.1177
## sex_0~~poshee_0c                  -0.0335  0.0271 20000  -0.0861    0.0198
## sex_0~~swesourceNA_0c             -0.0759  0.0371 20000  -0.1487   -0.0029
## sex_0~~fam3_0                     -0.0253  0.0121 20000  -0.0491   -0.0018
## age_0c~~bmi_0c                    -1.6174  2.3992 20000  -6.2655    3.1189
## age_0c~~painvas_0c                -2.7714  1.0004 20000  -4.7315   -0.8217
## age_0c~~poshee_0c                 -1.3036  0.4316 20000  -2.1483   -0.4584
## age_0c~~swesourceNA_0c            -1.0876  0.5828 20000  -2.2317    0.0630
## age_0c~~fam3_0                    -0.0283  0.1895 20000  -0.3999    0.3470
## bmi_0c~~painvas_0c                 1.8278  0.6441 20000   0.5592    3.0900
## bmi_0c~~poshee_0c                  0.0636  0.2744 20000  -0.4722    0.6027
## bmi_0c~~swesourceNA_0c             0.6648  0.3754 20000  -0.0637    1.4045
## bmi_0c~~fam3_0                     0.0555  0.1201 20000  -0.1805    0.2873
## painvas_0c~~poshee_0c             -0.0220  0.1203 20000  -0.2568    0.2150
## painvas_0c~~swesourceNA_0c         0.3541  0.1543 20000   0.0558    0.6542
## painvas_0c~~fam3_0                 0.0615  0.0514 20000  -0.0379    0.1626
## poshee_0c~~swesourceNA_0c         -0.1239  0.0661 20000  -0.2542    0.0060
## poshee_0c~~fam3_0                  0.0029  0.0213 20000  -0.0392    0.0443
## swesourceNA_0c~~fam3_0             0.0058  0.0293 20000  -0.0511    0.0638
## acplan_12c~~acplan_12c             2.1684  0.2426 20000   1.6892    2.6392
## muskeltraint_24~~muskeltraint_24 503.0759 54.5658 20000 395.1700  608.5814
## groupfu~~groupfu                   0.2499  0.0226 20000   0.2052    0.2942
## acplan_0c~~acplan_0c               3.0985  0.2810 20000   2.5412    3.6490
## muskeltraint_0c~~muskeltraint_0c 850.7023 77.2484 20000 698.4111 1000.0033
## HIE~~HIE                           0.2231  0.0203 20000   0.1828    0.2628
## LIE~~LIE                           0.2245  0.0204 20000   0.1843    0.2643
## sex_0~~sex_0                       0.2340  0.0211 20000   0.1927    0.2756
## age_0c~~age_0c                    57.7334  5.2337 20000  47.4282   68.0182
## bmi_0c~~bmi_0c                    23.6379  2.1597 20000  19.3208   27.7986
## painvas_0c~~painvas_0c             3.9731  0.3687 20000   3.2459    4.6946
## poshee_0c~~poshee_0c               0.7569  0.0689 20000   0.6229    0.8922
## swesourceNA_0c~~swesourceNA_0c     1.3505  0.1267 20000   1.1032    1.5978
## fam3_0~~fam3_0                     0.1470  0.0135 20000   0.1210    0.1734
## acplan_12c~1                      -0.6106  0.2549 20000  -1.1061   -0.1003
## muskeltraint_24~1                 22.6914  3.8991 20000  15.0553   30.4371
## groupfu~1                          0.5104  0.0322 20000   0.4475    0.5741
## acplan_0c~1                        0.0000  0.1126 20000  -0.2189    0.2209
## muskeltraint_0c~1                  0.0000  1.8678 20000  -3.6058    3.6723
## HIE~1                              0.3361  0.0305 20000   0.2763    0.3958
## LIE~1                              0.3402  0.0305 20000   0.2806    0.3999
## sex_0~1                            0.3734  0.0311 20000   0.3125    0.4340
## age_0c~1                           0.0000  0.4885 20000  -0.9421    0.9723
## bmi_0c~1                           0.0000  0.3158 20000  -0.6164    0.6149
## painvas_0c~1                      -0.0119  0.1284 20000  -0.2644    0.2383
## poshee_0c~1                        0.0000  0.0560 20000  -0.1092    0.1104
## swesourceNA_0c~1                  -0.0014  0.0763 20000  -0.1496    0.1500
## fam3_0~1                           0.1789  0.0248 20000   0.1300    0.2269
## ind                               -0.4059  0.6506 20000  -1.8980    0.7846
## total                              4.1113  3.5142 20000  -2.8986   10.9239

Action Planning at 18 Months

model <- '
# Direct Effects
acplan_18c ~ a1*groupfu + a2*acplan_0c + a3*muskeltraint_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
muskeltraint_24 ~ c1*groupfu + c2*acplan_0c + c3*muskeltraint_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*acplan_18c

# Covariances
groupfu ~~ acplan_0c + muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
acplan_0c ~~ muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
muskeltraint_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 429 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        12
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   acplan_18c ~                                                           
##     groupfu   (a1)     0.443    0.258    1.719    0.086   -0.062    0.948
##     acpln_0c  (a2)     0.164    0.074    2.214    0.027    0.019    0.309
##     mskltr_0  (a3)     0.011    0.006    1.761    0.078   -0.001    0.023
##     HIE       (a4)     0.215    0.310    0.692    0.489   -0.393    0.823
##     LIE       (a5)     0.015    0.314    0.048    0.962   -0.600    0.630
##     sex_0     (a6)     0.160    0.264    0.607    0.544   -0.357    0.678
##     age_0c    (a7)     0.015    0.019    0.821    0.412   -0.021    0.052
##     bmi_0c    (a8)     0.008    0.028    0.286    0.775   -0.046    0.062
##     panvs_0c  (a9)     0.060    0.072    0.836    0.403   -0.081    0.201
##     poshe_0c (a10)     0.222    0.149    1.485    0.137   -0.071    0.514
##     swsrNA_0 (a11)    -0.116    0.132   -0.878    0.380   -0.373    0.142
##     fam3_0   (a12)     0.306    0.330    0.929    0.353   -0.340    0.953
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     3.804    3.612    1.053    0.292   -3.275   10.884
##     acpln_0c  (c2)     2.304    1.030    2.235    0.025    0.284    4.323
##     mskltr_0  (c3)     0.439    0.075    5.839    0.000    0.292    0.586
##     HIE       (c4)    -4.684    4.306   -1.088    0.277  -13.123    3.755
##     LIE       (c5)     1.068    4.355    0.245    0.806   -7.467    9.604
##     sex_0     (c6)    -2.370    3.652   -0.649    0.516   -9.528    4.788
##     age_0c    (c7)     0.904    0.255    3.551    0.000    0.405    1.403
##     bmi_0c    (c8)    -0.102    0.383   -0.266    0.790   -0.853    0.649
##     panvs_0c  (c9)     2.057    0.954    2.156    0.031    0.187    3.926
##     poshe_0c (c10)     2.201    2.091    1.053    0.292   -1.897    6.300
##     swsrNA_0 (c11)    -2.777    1.702   -1.632    0.103   -6.112    0.558
##     fam3_0   (c12)    -3.309    4.755   -0.696    0.487  -12.629    6.011
##     acpln_18  (b1)     0.551    1.286    0.429    0.668   -1.969    3.071
## 
## Covariances:
##                      Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                              
##     acplan_0c          -0.007    0.057   -0.132    0.895   -0.119    0.104
##     muskeltrant_0c     -1.338    0.943   -1.418    0.156   -3.186    0.511
##     HIE                 0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE                -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0               0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c             -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c             -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c         -0.153    0.066   -2.343    0.019   -0.282   -0.025
##     poshee_0c           0.003    0.028    0.114    0.910   -0.052    0.058
##     swesourceNA_0c      0.068    0.038    1.769    0.077   -0.007    0.143
##     fam3_0             -0.033    0.013   -2.607    0.009   -0.057   -0.008
##   acplan_0c ~~                                                            
##     muskeltrant_0c      7.249    3.340    2.170    0.030    0.703   13.796
##     HIE                 0.038    0.054    0.717    0.473   -0.067    0.144
##     LIE                 0.000    0.054    0.004    0.997   -0.105    0.105
##     sex_0              -0.013    0.055   -0.244    0.807   -0.121    0.094
##     age_0c             -0.075    0.862   -0.088    0.930   -1.764    1.613
##     bmi_0c             -1.099    0.556   -1.978    0.048   -2.189   -0.010
##     painvas_0c          0.047    0.228    0.207    0.836   -0.400    0.494
##     poshee_0c           0.252    0.101    2.506    0.012    0.055    0.449
##     swesourceNA_0c     -0.148    0.136   -1.092    0.275   -0.414    0.118
##     fam3_0              0.037    0.044    0.841    0.400   -0.049    0.122
##   muskeltraint_0c ~~                                                      
##     HIE                 0.537    0.888    0.605    0.545   -1.204    2.278
##     LIE                -0.258    0.890   -0.289    0.772   -2.003    1.487
##     sex_0              -0.916    0.911   -1.005    0.315   -2.701    0.869
##     age_0c             11.641   14.295    0.814    0.415  -16.377   39.659
##     bmi_0c            -24.200    9.267   -2.612    0.009  -42.362   -6.038
##     painvas_0c         -1.216    3.765   -0.323    0.747   -8.595    6.163
##     poshee_0c           2.153    1.640    1.312    0.189   -1.063    5.368
##     swesourceNA_0c     -2.639    2.217   -1.190    0.234   -6.984    1.707
##     fam3_0              1.073    0.724    1.482    0.138   -0.346    2.493
##   HIE ~~                                                                  
##     LIE                -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0              -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c              0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c              0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c         -0.024    0.061   -0.389    0.697   -0.144    0.096
##     poshee_0c           0.044    0.027    1.651    0.099   -0.008    0.097
##     swesourceNA_0c     -0.035    0.036   -0.976    0.329   -0.107    0.036
##     fam3_0              0.002    0.012    0.180    0.857   -0.021    0.025
##   LIE ~~                                                                  
##     sex_0               0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c             -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c             -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c          0.035    0.062    0.571    0.568   -0.086    0.156
##     poshee_0c          -0.040    0.027   -1.490    0.136   -0.092    0.013
##     swesourceNA_0c      0.009    0.036    0.261    0.794   -0.061    0.080
##     fam3_0             -0.006    0.012   -0.547    0.585   -0.029    0.017
##   sex_0 ~~                                                                
##     age_0c              0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c             -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c         -0.007    0.063   -0.108    0.914   -0.130    0.116
##     poshee_0c          -0.034    0.027   -1.238    0.216   -0.087    0.020
##     swesourceNA_0c     -0.074    0.037   -2.003    0.045   -0.147   -0.002
##     fam3_0             -0.025    0.012   -2.098    0.036   -0.049   -0.002
##   age_0c ~~                                                               
##     bmi_0c             -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c         -2.763    0.996   -2.773    0.006   -4.716   -0.810
##     poshee_0c          -1.302    0.434   -3.000    0.003   -2.153   -0.451
##     swesourceNA_0c     -1.093    0.581   -1.881    0.060   -2.231    0.046
##     fam3_0             -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                               
##     painvas_0c          1.820    0.643    2.832    0.005    0.560    3.079
##     poshee_0c           0.068    0.274    0.249    0.803   -0.469    0.606
##     swesourceNA_0c      0.640    0.375    1.707    0.088   -0.095    1.374
##     fam3_0              0.055    0.121    0.460    0.646   -0.181    0.292
##   painvas_0c ~~                                                           
##     poshee_0c          -0.026    0.120   -0.220    0.826   -0.262    0.209
##     swesourceNA_0c      0.352    0.153    2.292    0.022    0.051    0.652
##     fam3_0              0.061    0.051    1.209    0.227   -0.038    0.161
##   poshee_0c ~~                                                            
##     swesourceNA_0c     -0.125    0.067   -1.881    0.060   -0.256    0.005
##     fam3_0              0.003    0.022    0.128    0.898   -0.039    0.045
##   swesourceNA_0c ~~                                                       
##     fam3_0              0.006    0.029    0.212    0.832   -0.051    0.064
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_18c       -0.371    0.272   -1.365    0.172   -0.905    0.162
##    .muskeltrant_24   23.437    3.884    6.034    0.000   15.824   31.050
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     acplan_0c         0.000    0.113    0.000    1.000   -0.222    0.222
##     muskeltrant_0c    0.000    1.879    0.000    1.000   -3.682    3.682
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c            0.000    0.489    0.000    1.000   -0.959    0.959
##     bmi_0c           -0.000    0.313   -0.000    1.000   -0.614    0.614
##     painvas_0c       -0.011    0.130   -0.083    0.934   -0.265    0.243
##     poshee_0c         0.001    0.056    0.010    0.992   -0.109    0.111
##     swesourceNA_0c   -0.001    0.076   -0.013    0.989   -0.151    0.149
##     fam3_0            0.179    0.025    7.230    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .acplan_18c        2.341    0.270    8.684    0.000    1.812    2.869
##    .muskeltrant_24  504.069   54.561    9.239    0.000  397.132  611.006
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     acplan_0c         3.099    0.282   10.977    0.000    2.545    3.652
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.969    0.366   10.854    0.000    3.252    4.686
##     poshee_0c         0.757    0.069   10.958    0.000    0.622    0.892
##     swesourceNA_0c    1.349    0.126   10.748    0.000    1.103    1.595
##     fam3_0            0.147    0.013   10.956    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.244    0.596    0.409    0.682   -0.925    1.413
##     total             4.049    3.548    1.141    0.254   -2.906   11.003

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                 0.4430  0.2578 20000  -0.0626    0.9537
## a2                                 0.1639  0.0750 20000   0.0160    0.3098
## a3                                 0.0108  0.0061 20000  -0.0012    0.0226
## a4                                 0.2148  0.3111 20000  -0.3924    0.8301
## a5                                 0.0149  0.3139 20000  -0.5981    0.6344
## a6                                 0.1602  0.2643 20000  -0.3655    0.6749
## a7                                 0.0153  0.0187 20000  -0.0216    0.0518
## a8                                 0.0079  0.0274 20000  -0.0454    0.0626
## a9                                 0.0601  0.0717 20000  -0.0813    0.2003
## a10                                0.2218  0.1494 20000  -0.0674    0.5171
## a11                               -0.1155  0.1303 20000  -0.3691    0.1408
## a12                                0.3064  0.3286 20000  -0.3411    0.9454
## c1                                 3.8045  3.5835 20000  -3.1780   10.9034
## c2                                 2.3036  1.0381 20000   0.2526    4.3155
## c3                                 0.4388  0.0748 20000   0.2939    0.5857
## c4                                -4.6843  4.3002 20000 -12.9929    3.8191
## c5                                 1.0684  4.3424 20000  -7.3729    9.6253
## c6                                -2.3702  3.6355 20000  -9.4032    4.7989
## c7                                 0.9039  0.2537 20000   0.4082    1.3983
## c8                                -0.1020  0.3822 20000  -0.8601    0.6483
## c9                                 2.0566  0.9584 20000   0.1753    3.9367
## c10                                2.2014  2.1110 20000  -1.8403    6.4251
## c11                               -2.7771  1.7108 20000  -6.1755    0.5361
## c12                               -3.3092  4.6949 20000 -12.3990    5.7916
## b1                                 0.5511  1.2944 20000  -1.9856    3.0680
## groupfu~~acplan_0c                -0.0075  0.0564 20000  -0.1159    0.1037
## groupfu~~muskeltraint_0c          -1.3377  0.9397 20000  -3.1797    0.4845
## groupfu~~HIE                       0.0069  0.0152 20000  -0.0229    0.0367
## groupfu~~LIE                      -0.0035  0.0152 20000  -0.0334    0.0261
## groupfu~~sex_0                     0.0003  0.0156 20000  -0.0305    0.0307
## groupfu~~age_0c                   -0.0706  0.2458 20000  -0.5553    0.4118
## groupfu~~bmi_0c                   -0.0398  0.1570 20000  -0.3450    0.2697
## groupfu~~painvas_0c               -0.1535  0.0659 20000  -0.2829   -0.0241
## groupfu~~poshee_0c                 0.0032  0.0280 20000  -0.0519    0.0581
## groupfu~~swesourceNA_0c            0.0680  0.0380 20000  -0.0070    0.1420
## groupfu~~fam3_0                   -0.0327  0.0127 20000  -0.0578   -0.0080
## acplan_0c~~muskeltraint_0c         7.2492  3.3390 20000   0.5441   13.8096
## acplan_0c~~HIE                     0.0385  0.0534 20000  -0.0657    0.1445
## acplan_0c~~LIE                     0.0002  0.0538 20000  -0.1045    0.1053
## acplan_0c~~sex_0                  -0.0134  0.0553 20000  -0.1198    0.0963
## acplan_0c~~age_0c                 -0.0755  0.8635 20000  -1.7483    1.6189
## acplan_0c~~bmi_0c                 -1.0993  0.5533 20000  -2.1785   -0.0193
## acplan_0c~~painvas_0c              0.0471  0.2279 20000  -0.3978    0.4953
## acplan_0c~~poshee_0c               0.2519  0.1004 20000   0.0558    0.4501
## acplan_0c~~swesourceNA_0c         -0.1480  0.1365 20000  -0.4176    0.1157
## acplan_0c~~fam3_0                  0.0367  0.0438 20000  -0.0495    0.1219
## muskeltraint_0c~~HIE               0.5372  0.8887 20000  -1.2045    2.2733
## muskeltraint_0c~~LIE              -0.2575  0.8916 20000  -2.0123    1.4888
## muskeltraint_0c~~sex_0            -0.9157  0.9100 20000  -2.7082    0.8604
## muskeltraint_0c~~age_0c           11.6412 14.2983 20000 -16.2845   39.9730
## muskeltraint_0c~~bmi_0c          -24.2002  9.2604 20000 -42.1836   -6.0587
## muskeltraint_0c~~painvas_0c       -1.2158  3.7937 20000  -8.7192    6.1063
## muskeltraint_0c~~poshee_0c         2.1528  1.6519 20000  -1.0936    5.3542
## muskeltraint_0c~~swesourceNA_0c   -2.6385  2.2231 20000  -7.0126    1.7181
## muskeltraint_0c~~fam3_0            1.0734  0.7219 20000  -0.3281    2.4934
## HIE~~LIE                          -0.1144  0.0161 20000  -0.1460   -0.0832
## HIE~~sex_0                        -0.0010  0.0147 20000  -0.0298    0.0277
## HIE~~age_0c                        0.1878  0.2293 20000  -0.2622    0.6325
## HIE~~bmi_0c                        0.3432  0.1496 20000   0.0510    0.6332
## HIE~~painvas_0c                   -0.0238  0.0612 20000  -0.1440    0.0963
## HIE~~poshee_0c                     0.0441  0.0267 20000  -0.0086    0.0960
## HIE~~swesourceNA_0c               -0.0354  0.0361 20000  -0.1063    0.0359
## HIE~~fam3_0                        0.0021  0.0117 20000  -0.0209    0.0247
## LIE~~sex_0                         0.0016  0.0149 20000  -0.0278    0.0309
## LIE~~age_0c                       -0.2255  0.2320 20000  -0.6751    0.2376
## LIE~~bmi_0c                       -0.3705  0.1507 20000  -0.6703   -0.0737
## LIE~~painvas_0c                    0.0352  0.0620 20000  -0.0866    0.1549
## LIE~~poshee_0c                    -0.0398  0.0267 20000  -0.0922    0.0124
## LIE~~swesourceNA_0c                0.0094  0.0362 20000  -0.0621    0.0801
## LIE~~fam3_0                       -0.0064  0.0117 20000  -0.0291    0.0168
## sex_0~~age_0c                      0.0533  0.2364 20000  -0.4107    0.5175
## sex_0~~bmi_0c                     -0.1011  0.1519 20000  -0.3989    0.1940
## sex_0~~painvas_0c                 -0.0068  0.0625 20000  -0.1290    0.1171
## sex_0~~poshee_0c                  -0.0337  0.0273 20000  -0.0870    0.0205
## sex_0~~swesourceNA_0c             -0.0745  0.0371 20000  -0.1470   -0.0017
## sex_0~~fam3_0                     -0.0253  0.0120 20000  -0.0489   -0.0018
## age_0c~~bmi_0c                    -1.6174  2.3940 20000  -6.3578    3.0726
## age_0c~~painvas_0c                -2.7630  0.9971 20000  -4.7135   -0.8021
## age_0c~~poshee_0c                 -1.3022  0.4294 20000  -2.1352   -0.4478
## age_0c~~swesourceNA_0c            -1.0926  0.5821 20000  -2.2241    0.0574
## age_0c~~fam3_0                    -0.0283  0.1870 20000  -0.3892    0.3360
## bmi_0c~~painvas_0c                 1.8199  0.6471 20000   0.5667    3.0997
## bmi_0c~~poshee_0c                  0.0684  0.2760 20000  -0.4690    0.6093
## bmi_0c~~swesourceNA_0c             0.6397  0.3740 20000  -0.0802    1.3876
## bmi_0c~~fam3_0                     0.0554  0.1199 20000  -0.1816    0.2897
## painvas_0c~~poshee_0c             -0.0265  0.1205 20000  -0.2636    0.2067
## painvas_0c~~swesourceNA_0c         0.3516  0.1528 20000   0.0472    0.6501
## painvas_0c~~fam3_0                 0.0614  0.0510 20000  -0.0386    0.1610
## poshee_0c~~swesourceNA_0c         -0.1254  0.0668 20000  -0.2545    0.0053
## poshee_0c~~fam3_0                  0.0028  0.0215 20000  -0.0398    0.0443
## swesourceNA_0c~~fam3_0             0.0062  0.0291 20000  -0.0506    0.0631
## acplan_18c~~acplan_18c             2.3407  0.2710 20000   1.8068    2.8683
## muskeltraint_24~~muskeltraint_24 504.0693 54.6636 20000 398.3748  612.1674
## groupfu~~groupfu                   0.2499  0.0227 20000   0.2057    0.2947
## acplan_0c~~acplan_0c               3.0985  0.2806 20000   2.5462    3.6489
## muskeltraint_0c~~muskeltraint_0c 850.7023 77.2493 20000 701.3231 1002.8990
## HIE~~HIE                           0.2231  0.0204 20000   0.1836    0.2631
## LIE~~LIE                           0.2245  0.0205 20000   0.1842    0.2651
## sex_0~~sex_0                       0.2340  0.0212 20000   0.1922    0.2754
## age_0c~~age_0c                    57.7334  5.2342 20000  47.4446   67.9820
## bmi_0c~~bmi_0c                    23.6379  2.1660 20000  19.3510   27.8214
## painvas_0c~~painvas_0c             3.9690  0.3673 20000   3.2440    4.6828
## poshee_0c~~poshee_0c               0.7570  0.0695 20000   0.6211    0.8952
## swesourceNA_0c~~swesourceNA_0c     1.3492  0.1267 20000   1.1004    1.5951
## fam3_0~~fam3_0                     0.1470  0.0135 20000   0.1203    0.1736
## acplan_18c~1                      -0.3714  0.2723 20000  -0.9062    0.1633
## muskeltraint_24~1                 23.4370  3.8848 20000  15.7716   30.9623
## groupfu~1                          0.5104  0.0322 20000   0.4475    0.5731
## acplan_0c~1                        0.0000  0.1128 20000  -0.2198    0.2209
## muskeltraint_0c~1                  0.0000  1.8678 20000  -3.6726    3.6048
## HIE~1                              0.3361  0.0305 20000   0.2764    0.3955
## LIE~1                              0.3402  0.0306 20000   0.2799    0.4000
## sex_0~1                            0.3734  0.0310 20000   0.3133    0.4346
## age_0c~1                           0.0000  0.4885 20000  -0.9702    0.9430
## bmi_0c~1                           0.0000  0.3158 20000  -0.6146    0.6192
## painvas_0c~1                      -0.0107  0.1293 20000  -0.2637    0.2441
## poshee_0c~1                        0.0006  0.0562 20000  -0.1101    0.1107
## swesourceNA_0c~1                  -0.0010  0.0762 20000  -0.1484    0.1500
## fam3_0~1                           0.1789  0.0250 20000   0.1308    0.2285
## ind                                0.2442  0.6792 20000  -0.9813    1.8642
## total                              4.0486  3.5306 20000  -2.8117   11.0272

Coping Planning at 12 Months

model <- '
# Direct Effects
coplan_12c ~ a1*groupfu + a2*coplan_0c + a3*muskeltraint_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
muskeltraint_24 ~ c1*groupfu + c2*coplan_0c + c3*muskeltraint_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*coplan_12c

# Covariances
groupfu ~~ coplan_0c + muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
coplan_0c ~~ muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
muskeltraint_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 426 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        12
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   coplan_12c ~                                                           
##     groupfu   (a1)     0.084    0.217    0.388    0.698   -0.342    0.510
##     copln_0c  (a2)     0.371    0.075    4.975    0.000    0.225    0.517
##     mskltr_0  (a3)    -0.001    0.005   -0.208    0.835   -0.011    0.009
##     HIE       (a4)     0.181    0.261    0.692    0.489   -0.331    0.692
##     LIE       (a5)     0.098    0.264    0.372    0.710   -0.419    0.615
##     sex_0     (a6)     0.060    0.222    0.270    0.787   -0.376    0.496
##     age_0c    (a7)     0.020    0.016    1.243    0.214   -0.012    0.052
##     bmi_0c    (a8)     0.017    0.023    0.753    0.452   -0.028    0.063
##     panvs_0c  (a9)     0.033    0.059    0.559    0.576   -0.083    0.149
##     poshe_0c (a10)     0.257    0.130    1.979    0.048    0.002    0.511
##     swsrNA_0 (a11)     0.003    0.109    0.025    0.980   -0.210    0.216
##     fam3_0   (a12)     0.116    0.281    0.412    0.681   -0.435    0.667
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     3.912    3.597    1.088    0.277   -3.137   10.962
##     copln_0c  (c2)     1.534    1.347    1.139    0.255   -1.106    4.174
##     mskltr_0  (c3)     0.446    0.075    5.916    0.000    0.298    0.594
##     HIE       (c4)    -4.119    4.352   -0.946    0.344  -12.649    4.412
##     LIE       (c5)     0.548    4.410    0.124    0.901   -8.094    9.191
##     sex_0     (c6)    -1.853    3.693   -0.502    0.616   -9.092    5.385
##     age_0c    (c7)     0.887    0.269    3.300    0.001    0.360    1.414
##     bmi_0c    (c8)    -0.145    0.390   -0.372    0.710   -0.909    0.618
##     panvs_0c  (c9)     2.106    0.975    2.160    0.031    0.195    4.017
##     poshe_0c (c10)     2.933    2.125    1.380    0.168   -1.233    7.098
##     swsrNA_0 (c11)    -2.570    1.718   -1.495    0.135   -5.938    0.798
##     fam3_0   (c12)    -2.940    4.825   -0.609    0.542  -12.397    6.517
##     cpln_12c  (b1)    -0.164    1.382   -0.119    0.905   -2.872    2.544
## 
## Covariances:
##                      Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                              
##     coplan_0c          -0.019    0.051   -0.378    0.705   -0.119    0.080
##     muskeltrant_0c     -1.338    0.943   -1.418    0.156   -3.186    0.511
##     HIE                 0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE                -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0               0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c             -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c             -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c         -0.155    0.066   -2.368    0.018   -0.284   -0.027
##     poshee_0c           0.003    0.028    0.092    0.927   -0.052    0.058
##     swesourceNA_0c      0.069    0.038    1.781    0.075   -0.007    0.144
##     fam3_0             -0.033    0.013   -2.606    0.009   -0.057   -0.008
##   coplan_0c ~~                                                            
##     muskeltrant_0c      8.265    3.008    2.748    0.006    2.370   14.160
##     HIE                -0.040    0.048   -0.825    0.409   -0.134    0.055
##     LIE                 0.039    0.048    0.814    0.415   -0.055    0.134
##     sex_0              -0.091    0.050   -1.839    0.066   -0.188    0.006
##     age_0c              2.385    0.786    3.033    0.002    0.844    3.926
##     bmi_0c             -1.348    0.501   -2.690    0.007   -2.331   -0.366
##     painvas_0c          0.145    0.205    0.705    0.481   -0.257    0.547
##     poshee_0c           0.201    0.090    2.239    0.025    0.025    0.377
##     swesourceNA_0c     -0.133    0.121   -1.101    0.271   -0.371    0.104
##     fam3_0              0.023    0.039    0.586    0.558   -0.054    0.099
##   muskeltraint_0c ~~                                                      
##     HIE                 0.537    0.888    0.605    0.545   -1.204    2.278
##     LIE                -0.258    0.890   -0.289    0.772   -2.003    1.487
##     sex_0              -0.916    0.911   -1.005    0.315   -2.701    0.869
##     age_0c             11.641   14.295    0.814    0.415  -16.377   39.659
##     bmi_0c            -24.200    9.267   -2.612    0.009  -42.362   -6.038
##     painvas_0c         -1.208    3.768   -0.321    0.748   -8.593    6.176
##     poshee_0c           2.154    1.640    1.313    0.189   -1.061    5.369
##     swesourceNA_0c     -2.625    2.217   -1.184    0.237   -6.971    1.722
##     fam3_0              1.073    0.724    1.481    0.138   -0.347    2.493
##   HIE ~~                                                                  
##     LIE                -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0              -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c              0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c              0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c         -0.022    0.061   -0.360    0.719   -0.142    0.098
##     poshee_0c           0.043    0.027    1.621    0.105   -0.009    0.096
##     swesourceNA_0c     -0.035    0.036   -0.969    0.332   -0.106    0.036
##     fam3_0              0.002    0.012    0.179    0.858   -0.021    0.025
##   LIE ~~                                                                  
##     sex_0               0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c             -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c             -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c          0.034    0.062    0.557    0.578   -0.087    0.155
##     poshee_0c          -0.039    0.027   -1.474    0.140   -0.092    0.013
##     swesourceNA_0c      0.009    0.036    0.259    0.795   -0.061    0.080
##     fam3_0             -0.006    0.012   -0.545    0.585   -0.029    0.017
##   sex_0 ~~                                                                
##     age_0c              0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c             -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c         -0.006    0.063   -0.096    0.924   -0.129    0.117
##     poshee_0c          -0.033    0.027   -1.221    0.222   -0.087    0.020
##     swesourceNA_0c     -0.075    0.037   -2.014    0.044   -0.148   -0.002
##     fam3_0             -0.025    0.012   -2.099    0.036   -0.049   -0.002
##   age_0c ~~                                                               
##     bmi_0c             -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c         -2.757    0.997   -2.765    0.006   -4.711   -0.803
##     poshee_0c          -1.305    0.434   -3.006    0.003   -2.156   -0.454
##     swesourceNA_0c     -1.088    0.581   -1.872    0.061   -2.226    0.051
##     fam3_0             -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                               
##     painvas_0c          1.845    0.643    2.868    0.004    0.584    3.106
##     poshee_0c           0.058    0.274    0.212    0.832   -0.479    0.595
##     swesourceNA_0c      0.651    0.375    1.737    0.082   -0.084    1.385
##     fam3_0              0.056    0.121    0.461    0.645   -0.181    0.292
##   painvas_0c ~~                                                           
##     poshee_0c          -0.025    0.120   -0.206    0.837   -0.261    0.211
##     swesourceNA_0c      0.351    0.154    2.288    0.022    0.050    0.652
##     fam3_0              0.063    0.051    1.242    0.214   -0.037    0.163
##   poshee_0c ~~                                                            
##     swesourceNA_0c     -0.125    0.067   -1.868    0.062   -0.255    0.006
##     fam3_0              0.003    0.022    0.138    0.890   -0.039    0.045
##   swesourceNA_0c ~~                                                       
##     fam3_0              0.006    0.029    0.196    0.844   -0.052    0.063
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_12c       -0.169    0.229   -0.739    0.460   -0.619    0.280
##    .muskeltrant_24   23.205    3.893    5.960    0.000   15.574   30.836
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     coplan_0c         0.001    0.102    0.010    0.992   -0.198    0.200
##     muskeltrant_0c    0.000    1.879    0.000    1.000   -3.682    3.682
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c            0.000    0.489    0.000    1.000   -0.959    0.959
##     bmi_0c           -0.000    0.313   -0.000    1.000   -0.614    0.614
##     painvas_0c       -0.011    0.130   -0.085    0.932   -0.265    0.243
##     poshee_0c        -0.001    0.056   -0.012    0.991   -0.111    0.109
##     swesourceNA_0c   -0.002    0.076   -0.021    0.983   -0.151    0.148
##     fam3_0            0.179    0.025    7.231    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_12c        1.772    0.198    8.967    0.000    1.385    2.159
##    .muskeltrant_24  517.703   56.025    9.241    0.000  407.896  627.511
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     coplan_0c         2.482    0.226   10.965    0.000    2.039    2.926
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.975    0.367   10.839    0.000    3.256    4.694
##     poshee_0c         0.757    0.069   10.959    0.000    0.622    0.892
##     swesourceNA_0c    1.350    0.126   10.746    0.000    1.103    1.596
##     fam3_0            0.147    0.013   10.956    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.014    0.120   -0.115    0.908   -0.250    0.222
##     total             3.898    3.598    1.084    0.279   -3.153   10.950

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                 0.0843  0.2175 20000  -0.3417    0.5072
## a2                                 0.3712  0.0745 20000   0.2257    0.5164
## a3                                -0.0011  0.0051 20000  -0.0109    0.0089
## a4                                 0.1805  0.2610 20000  -0.3259    0.6958
## a5                                 0.0981  0.2646 20000  -0.4148    0.6215
## a6                                 0.0601  0.2233 20000  -0.3770    0.4902
## a7                                 0.0200  0.0161 20000  -0.0114    0.0514
## a8                                 0.0175  0.0233 20000  -0.0287    0.0627
## a9                                 0.0330  0.0592 20000  -0.0834    0.1477
## a10                                0.2569  0.1300 20000   0.0051    0.5130
## a11                                0.0027  0.1084 20000  -0.2118    0.2155
## a12                                0.1157  0.2833 20000  -0.4407    0.6720
## c1                                 3.9123  3.5861 20000  -3.1287   10.9969
## c2                                 1.5340  1.3494 20000  -1.1060    4.1408
## c3                                 0.4461  0.0755 20000   0.2973    0.5926
## c4                                -4.1187  4.3351 20000 -12.8367    4.3631
## c5                                 0.5481  4.4106 20000  -8.1268    9.1961
## c6                                -1.8535  3.6652 20000  -9.0313    5.4354
## c7                                 0.8868  0.2690 20000   0.3645    1.4133
## c8                                -0.1451  0.3862 20000  -0.9072    0.6053
## c9                                 2.1059  0.9770 20000   0.1837    4.0169
## c10                                2.9326  2.1434 20000  -1.1993    7.1982
## c11                               -2.5700  1.7219 20000  -5.9923    0.7573
## c12                               -2.9402  4.7530 20000 -12.3133    6.3428
## b1                                -0.1643  1.3832 20000  -2.8717    2.5081
## groupfu~~coplan_0c                -0.0192  0.0508 20000  -0.1199    0.0794
## groupfu~~muskeltraint_0c          -1.3377  0.9442 20000  -3.1898    0.5156
## groupfu~~HIE                       0.0069  0.0152 20000  -0.0231    0.0365
## groupfu~~LIE                      -0.0035  0.0152 20000  -0.0333    0.0261
## groupfu~~sex_0                     0.0003  0.0155 20000  -0.0301    0.0307
## groupfu~~age_0c                   -0.0706  0.2477 20000  -0.5590    0.4156
## groupfu~~bmi_0c                   -0.0398  0.1573 20000  -0.3450    0.2681
## groupfu~~painvas_0c               -0.1553  0.0658 20000  -0.2846   -0.0266
## groupfu~~poshee_0c                 0.0026  0.0279 20000  -0.0525    0.0571
## groupfu~~swesourceNA_0c            0.0685  0.0385 20000  -0.0066    0.1437
## groupfu~~fam3_0                   -0.0327  0.0126 20000  -0.0576   -0.0080
## coplan_0c~~muskeltraint_0c         8.2654  3.0407 20000   2.3741   14.2626
## coplan_0c~~HIE                    -0.0396  0.0484 20000  -0.1342    0.0543
## coplan_0c~~LIE                     0.0393  0.0480 20000  -0.0547    0.1330
## coplan_0c~~sex_0                  -0.0912  0.0497 20000  -0.1876    0.0060
## coplan_0c~~age_0c                  2.3853  0.7855 20000   0.8477    3.9103
## coplan_0c~~bmi_0c                 -1.3484  0.5027 20000  -2.3332   -0.3733
## coplan_0c~~painvas_0c              0.1447  0.2049 20000  -0.2664    0.5429
## coplan_0c~~poshee_0c               0.2009  0.0893 20000   0.0236    0.3741
## coplan_0c~~swesourceNA_0c         -0.1334  0.1207 20000  -0.3722    0.1017
## coplan_0c~~fam3_0                  0.0228  0.0391 20000  -0.0536    0.0997
## muskeltraint_0c~~HIE               0.5372  0.8923 20000  -1.2137    2.2647
## muskeltraint_0c~~LIE              -0.2575  0.8889 20000  -2.0020    1.4599
## muskeltraint_0c~~sex_0            -0.9157  0.9105 20000  -2.7076    0.8603
## muskeltraint_0c~~age_0c           11.6412 14.2952 20000 -16.2813   39.9402
## muskeltraint_0c~~bmi_0c          -24.2002  9.2591 20000 -42.1600   -5.9469
## muskeltraint_0c~~painvas_0c       -1.2084  3.7951 20000  -8.6314    6.3310
## muskeltraint_0c~~poshee_0c         2.1538  1.6501 20000  -1.0715    5.4137
## muskeltraint_0c~~swesourceNA_0c   -2.6246  2.2272 20000  -6.9765    1.7776
## muskeltraint_0c~~fam3_0            1.0730  0.7232 20000  -0.3279    2.5042
## HIE~~LIE                          -0.1144  0.0160 20000  -0.1453   -0.0832
## HIE~~sex_0                        -0.0010  0.0147 20000  -0.0298    0.0278
## HIE~~age_0c                        0.1878  0.2291 20000  -0.2613    0.6383
## HIE~~bmi_0c                        0.3432  0.1498 20000   0.0496    0.6355
## HIE~~painvas_0c                   -0.0220  0.0614 20000  -0.1414    0.0981
## HIE~~poshee_0c                     0.0433  0.0268 20000  -0.0091    0.0959
## HIE~~swesourceNA_0c               -0.0352  0.0364 20000  -0.1064    0.0360
## HIE~~fam3_0                        0.0021  0.0118 20000  -0.0209    0.0250
## LIE~~sex_0                         0.0016  0.0148 20000  -0.0275    0.0305
## LIE~~age_0c                       -0.2255  0.2322 20000  -0.6773    0.2319
## LIE~~bmi_0c                       -0.3705  0.1501 20000  -0.6644   -0.0766
## LIE~~painvas_0c                    0.0343  0.0622 20000  -0.0869    0.1571
## LIE~~poshee_0c                    -0.0394  0.0268 20000  -0.0919    0.0132
## LIE~~swesourceNA_0c                0.0094  0.0361 20000  -0.0616    0.0799
## LIE~~fam3_0                       -0.0064  0.0117 20000  -0.0292    0.0165
## sex_0~~age_0c                      0.0533  0.2380 20000  -0.4168    0.5202
## sex_0~~bmi_0c                     -0.1011  0.1504 20000  -0.4000    0.1937
## sex_0~~painvas_0c                 -0.0060  0.0630 20000  -0.1298    0.1167
## sex_0~~poshee_0c                  -0.0332  0.0272 20000  -0.0864    0.0193
## sex_0~~swesourceNA_0c             -0.0749  0.0373 20000  -0.1484   -0.0016
## sex_0~~fam3_0                     -0.0253  0.0122 20000  -0.0492   -0.0016
## age_0c~~bmi_0c                    -1.6174  2.3939 20000  -6.3604    3.0704
## age_0c~~painvas_0c                -2.7568  1.0018 20000  -4.7185   -0.7969
## age_0c~~poshee_0c                 -1.3052  0.4359 20000  -2.1498   -0.4365
## age_0c~~swesourceNA_0c            -1.0876  0.5840 20000  -2.2355    0.0597
## age_0c~~fam3_0                    -0.0283  0.1882 20000  -0.3987    0.3308
## bmi_0c~~painvas_0c                 1.8451  0.6433 20000   0.5954    3.1093
## bmi_0c~~poshee_0c                  0.0581  0.2725 20000  -0.4797    0.5928
## bmi_0c~~swesourceNA_0c             0.6510  0.3748 20000  -0.0742    1.3940
## bmi_0c~~fam3_0                     0.0556  0.1211 20000  -0.1824    0.2908
## painvas_0c~~poshee_0c             -0.0247  0.1204 20000  -0.2644    0.2106
## painvas_0c~~swesourceNA_0c         0.3512  0.1536 20000   0.0498    0.6549
## painvas_0c~~fam3_0                 0.0631  0.0511 20000  -0.0368    0.1626
## poshee_0c~~swesourceNA_0c         -0.1245  0.0667 20000  -0.2574    0.0057
## poshee_0c~~fam3_0                  0.0030  0.0214 20000  -0.0391    0.0447
## swesourceNA_0c~~fam3_0             0.0058  0.0294 20000  -0.0510    0.0636
## coplan_12c~~coplan_12c             1.7718  0.1983 20000   1.3802    2.1640
## muskeltraint_24~~muskeltraint_24 517.7034 56.1309 20000 409.1710  628.7026
## groupfu~~groupfu                   0.2499  0.0226 20000   0.2057    0.2942
## coplan_0c~~coplan_0c               2.4825  0.2254 20000   2.0453    2.9242
## muskeltraint_0c~~muskeltraint_0c 850.7023 77.2491 20000 701.2988 1002.9161
## HIE~~HIE                           0.2231  0.0204 20000   0.1832    0.2636
## LIE~~LIE                           0.2245  0.0205 20000   0.1842    0.2642
## sex_0~~sex_0                       0.2340  0.0211 20000   0.1926    0.2753
## age_0c~~age_0c                    57.7334  5.2342 20000  47.4116   67.9747
## bmi_0c~~bmi_0c                    23.6379  2.1666 20000  19.3486   27.8387
## painvas_0c~~painvas_0c             3.9753  0.3645 20000   3.2631    4.6889
## poshee_0c~~poshee_0c               0.7569  0.0692 20000   0.6224    0.8932
## swesourceNA_0c~~swesourceNA_0c     1.3496  0.1247 20000   1.1043    1.5906
## fam3_0~~fam3_0                     0.1470  0.0135 20000   0.1205    0.1736
## coplan_12c~1                      -0.1694  0.2287 20000  -0.6194    0.2767
## muskeltraint_24~1                 23.2048  3.8927 20000  15.5115   30.8773
## groupfu~1                          0.5104  0.0321 20000   0.4469    0.5732
## coplan_0c~1                        0.0010  0.1024 20000  -0.1983    0.2031
## muskeltraint_0c~1                  0.0000  1.8676 20000  -3.6693    3.6033
## HIE~1                              0.3361  0.0304 20000   0.2759    0.3956
## LIE~1                              0.3402  0.0302 20000   0.2805    0.3994
## sex_0~1                            0.3734  0.0312 20000   0.3124    0.4351
## age_0c~1                           0.0000  0.4883 20000  -0.9576    0.9480
## bmi_0c~1                           0.0000  0.3155 20000  -0.6156    0.6142
## painvas_0c~1                      -0.0110  0.1306 20000  -0.2701    0.2417
## poshee_0c~1                       -0.0007  0.0563 20000  -0.1110    0.1082
## swesourceNA_0c~1                  -0.0016  0.0766 20000  -0.1493    0.1502
## fam3_0~1                           0.1790  0.0246 20000   0.1311    0.2278
## ind                               -0.0139  0.3264 20000  -0.7184    0.7029
## total                              3.8984  3.6022 20000  -3.1377   11.0330

Coping Planning at 18 Months

model <- '
# Direct Effects
coplan_18c ~ a1*groupfu + a2*coplan_0c + a3*muskeltraint_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
muskeltraint_24 ~ c1*groupfu + c2*coplan_0c + c3*muskeltraint_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*coplan_18c

# Covariances
groupfu ~~ coplan_0c + muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
coplan_0c ~~ muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
muskeltraint_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 422 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        13
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   coplan_18c ~                                                           
##     groupfu   (a1)     0.292    0.215    1.362    0.173   -0.128    0.713
##     copln_0c  (a2)     0.279    0.073    3.840    0.000    0.137    0.422
##     mskltr_0  (a3)    -0.002    0.005   -0.372    0.710   -0.012    0.008
##     HIE       (a4)     0.541    0.257    2.104    0.035    0.037    1.044
##     LIE       (a5)     0.173    0.262    0.662    0.508   -0.340    0.687
##     sex_0     (a6)    -0.356    0.222   -1.605    0.108   -0.791    0.079
##     age_0c    (a7)     0.027    0.016    1.722    0.085   -0.004    0.059
##     bmi_0c    (a8)     0.063    0.023    2.725    0.006    0.018    0.108
##     panvs_0c  (a9)     0.169    0.060    2.812    0.005    0.051    0.287
##     poshe_0c (a10)     0.207    0.126    1.639    0.101   -0.041    0.455
##     swsrNA_0 (a11)    -0.117    0.110   -1.063    0.288   -0.332    0.098
##     fam3_0   (a12)     0.020    0.274    0.074    0.941   -0.517    0.557
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     4.248    3.627    1.171    0.241   -2.860   11.355
##     copln_0c  (c2)     1.835    1.324    1.386    0.166   -0.760    4.431
##     mskltr_0  (c3)     0.444    0.075    5.889    0.000    0.296    0.591
##     HIE       (c4)    -3.442    4.452   -0.773    0.439  -12.168    5.284
##     LIE       (c5)     0.827    4.417    0.187    0.851   -7.829    9.483
##     sex_0     (c6)    -2.173    3.718   -0.585    0.559   -9.459    5.113
##     age_0c    (c7)     0.913    0.269    3.387    0.001    0.384    1.441
##     bmi_0c    (c8)    -0.071    0.403   -0.177    0.860   -0.862    0.719
##     panvs_0c  (c9)     2.280    1.008    2.262    0.024    0.305    4.255
##     poshe_0c (c10)     3.134    2.106    1.488    0.137   -0.994    7.261
##     swsrNA_0 (c11)    -2.721    1.729   -1.574    0.116   -6.109    0.667
##     fam3_0   (c12)    -2.975    4.812   -0.618    0.536  -12.406    6.456
##     cpln_18c  (b1)    -1.170    1.573   -0.744    0.457   -4.253    1.912
## 
## Covariances:
##                      Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                              
##     coplan_0c          -0.019    0.051   -0.371    0.710   -0.119    0.081
##     muskeltrant_0c     -1.338    0.943   -1.418    0.156   -3.186    0.511
##     HIE                 0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE                -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0               0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c             -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c             -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c         -0.155    0.066   -2.359    0.018   -0.283   -0.026
##     poshee_0c           0.003    0.028    0.122    0.903   -0.052    0.058
##     swesourceNA_0c      0.067    0.038    1.746    0.081   -0.008    0.143
##     fam3_0             -0.033    0.013   -2.606    0.009   -0.057   -0.008
##   coplan_0c ~~                                                            
##     muskeltrant_0c      8.266    3.008    2.748    0.006    2.371   14.161
##     HIE                -0.039    0.048   -0.820    0.412   -0.134    0.055
##     LIE                 0.039    0.048    0.805    0.421   -0.056    0.134
##     sex_0              -0.092    0.050   -1.848    0.065   -0.189    0.006
##     age_0c              2.385    0.786    3.033    0.002    0.844    3.926
##     bmi_0c             -1.346    0.501   -2.685    0.007   -2.329   -0.364
##     painvas_0c          0.153    0.205    0.746    0.455   -0.249    0.555
##     poshee_0c           0.197    0.090    2.194    0.028    0.021    0.373
##     swesourceNA_0c     -0.131    0.121   -1.079    0.281   -0.368    0.107
##     fam3_0              0.023    0.039    0.590    0.555   -0.053    0.099
##   muskeltraint_0c ~~                                                      
##     HIE                 0.537    0.888    0.605    0.545   -1.204    2.278
##     LIE                -0.258    0.890   -0.289    0.772   -2.003    1.487
##     sex_0              -0.916    0.911   -1.005    0.315   -2.701    0.869
##     age_0c             11.641   14.295    0.814    0.415  -16.377   39.659
##     bmi_0c            -24.200    9.267   -2.612    0.009  -42.362   -6.038
##     painvas_0c         -1.104    3.765   -0.293    0.769   -8.483    6.274
##     poshee_0c           2.152    1.641    1.312    0.190   -1.063    5.368
##     swesourceNA_0c     -2.618    2.217   -1.181    0.238   -6.962    1.727
##     fam3_0              1.073    0.724    1.481    0.138   -0.347    2.493
##   HIE ~~                                                                  
##     LIE                -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0              -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c              0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c              0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c         -0.023    0.061   -0.370    0.711   -0.142    0.097
##     poshee_0c           0.044    0.027    1.663    0.096   -0.008    0.097
##     swesourceNA_0c     -0.036    0.036   -1.002    0.316   -0.108    0.035
##     fam3_0              0.002    0.012    0.179    0.858   -0.021    0.025
##   LIE ~~                                                                  
##     sex_0               0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c             -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c             -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c          0.034    0.062    0.555    0.579   -0.087    0.155
##     poshee_0c          -0.040    0.027   -1.496    0.135   -0.092    0.012
##     swesourceNA_0c      0.010    0.036    0.273    0.785   -0.061    0.081
##     fam3_0             -0.006    0.012   -0.545    0.585   -0.029    0.017
##   sex_0 ~~                                                                
##     age_0c              0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c             -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c         -0.007    0.063   -0.105    0.916   -0.129    0.116
##     poshee_0c          -0.034    0.027   -1.244    0.213   -0.087    0.019
##     swesourceNA_0c     -0.074    0.037   -1.995    0.046   -0.147   -0.001
##     fam3_0             -0.025    0.012   -2.099    0.036   -0.049   -0.002
##   age_0c ~~                                                               
##     bmi_0c             -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c         -2.748    0.996   -2.759    0.006   -4.700   -0.796
##     poshee_0c          -1.301    0.434   -2.997    0.003   -2.152   -0.450
##     swesourceNA_0c     -1.094    0.581   -1.883    0.060   -2.232    0.045
##     fam3_0             -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                               
##     painvas_0c          1.841    0.643    2.864    0.004    0.581    3.100
##     poshee_0c           0.072    0.274    0.264    0.792   -0.465    0.610
##     swesourceNA_0c      0.637    0.375    1.701    0.089   -0.097    1.371
##     fam3_0              0.056    0.121    0.461    0.645   -0.181    0.292
##   painvas_0c ~~                                                           
##     poshee_0c          -0.026    0.120   -0.214    0.831   -0.261    0.209
##     swesourceNA_0c      0.348    0.153    2.267    0.023    0.047    0.648
##     fam3_0              0.063    0.051    1.239    0.215   -0.037    0.162
##   poshee_0c ~~                                                            
##     swesourceNA_0c     -0.124    0.067   -1.868    0.062   -0.255    0.006
##     fam3_0              0.003    0.022    0.124    0.901   -0.039    0.045
##   swesourceNA_0c ~~                                                       
##     fam3_0              0.006    0.029    0.221    0.825   -0.051    0.064
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_18c       -0.276    0.226   -1.220    0.223   -0.719    0.167
##    .muskeltrant_24   22.815    3.922    5.818    0.000   15.129   30.502
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     coplan_0c         0.000    0.102    0.003    0.998   -0.199    0.200
##     muskeltrant_0c   -0.000    1.879   -0.000    1.000   -3.682    3.682
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c           -0.000    0.489   -0.000    1.000   -0.959    0.959
##     bmi_0c            0.000    0.313    0.000    1.000   -0.614    0.614
##     painvas_0c       -0.010    0.130   -0.077    0.938   -0.264    0.244
##     poshee_0c         0.001    0.056    0.019    0.985   -0.109    0.111
##     swesourceNA_0c   -0.004    0.076   -0.049    0.961   -0.154    0.146
##     fam3_0            0.179    0.025    7.231    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .coplan_18c        1.596    0.185    8.610    0.000    1.232    1.959
##    .muskeltrant_24  515.577   55.854    9.231    0.000  406.104  625.049
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     coplan_0c         2.483    0.226   10.964    0.000    2.039    2.926
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.971    0.366   10.852    0.000    3.254    4.688
##     poshee_0c         0.757    0.069   10.957    0.000    0.622    0.893
##     swesourceNA_0c    1.348    0.125   10.753    0.000    1.103    1.594
##     fam3_0            0.147    0.013   10.956    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.342    0.508   -0.673    0.501   -1.338    0.654
##     total             3.906    3.596    1.086    0.277   -3.142   10.953

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                 0.2923  0.2172 20000  -0.1389    0.7146
## a2                                 0.2792  0.0726 20000   0.1365    0.4203
## a3                                -0.0019  0.0050 20000  -0.0117    0.0079
## a4                                 0.5405  0.2577 20000   0.0355    1.0449
## a5                                 0.1735  0.2627 20000  -0.3451    0.6882
## a6                                -0.3560  0.2200 20000  -0.7861    0.0758
## a7                                 0.0275  0.0160 20000  -0.0037    0.0589
## a8                                 0.0629  0.0233 20000   0.0165    0.1077
## a9                                 0.1694  0.0607 20000   0.0516    0.2885
## a10                                0.2071  0.1270 20000  -0.0416    0.4544
## a11                               -0.1167  0.1100 20000  -0.3346    0.0976
## a12                                0.0204  0.2763 20000  -0.5225    0.5622
## c1                                 4.2476  3.6291 20000  -2.9609   11.3807
## c2                                 1.8355  1.3245 20000  -0.7394    4.4141
## c3                                 0.4437  0.0761 20000   0.2980    0.5962
## c4                                -3.4419  4.4336 20000 -12.1081    5.4349
## c5                                 0.8270  4.4101 20000  -7.8808    9.5132
## c6                                -2.1730  3.7388 20000  -9.4979    5.1896
## c7                                 0.9125  0.2681 20000   0.3931    1.4383
## c8                                -0.0712  0.3997 20000  -0.8508    0.7177
## c9                                 2.2797  1.0124 20000   0.3064    4.2757
## c10                                3.1336  2.1121 20000  -0.9647    7.3086
## c11                               -2.7209  1.7399 20000  -6.0768    0.7218
## c12                               -2.9750  4.7504 20000 -12.2713    6.3929
## b1                                -1.1704  1.5731 20000  -4.2742    1.9066
## groupfu~~coplan_0c                -0.0189  0.0510 20000  -0.1180    0.0810
## groupfu~~muskeltraint_0c          -1.3377  0.9372 20000  -3.1596    0.4985
## groupfu~~HIE                       0.0069  0.0152 20000  -0.0228    0.0366
## groupfu~~LIE                      -0.0035  0.0152 20000  -0.0340    0.0260
## groupfu~~sex_0                     0.0003  0.0155 20000  -0.0300    0.0305
## groupfu~~age_0c                   -0.0706  0.2463 20000  -0.5536    0.4106
## groupfu~~bmi_0c                   -0.0398  0.1576 20000  -0.3479    0.2651
## groupfu~~painvas_0c               -0.1545  0.0654 20000  -0.2836   -0.0260
## groupfu~~poshee_0c                 0.0034  0.0278 20000  -0.0505    0.0575
## groupfu~~swesourceNA_0c            0.0672  0.0383 20000  -0.0072    0.1422
## groupfu~~fam3_0                   -0.0327  0.0126 20000  -0.0573   -0.0082
## coplan_0c~~muskeltraint_0c         8.2660  3.0238 20000   2.3348   14.2283
## coplan_0c~~HIE                    -0.0394  0.0482 20000  -0.1329    0.0551
## coplan_0c~~LIE                     0.0389  0.0482 20000  -0.0572    0.1322
## coplan_0c~~sex_0                  -0.0916  0.0496 20000  -0.1878    0.0062
## coplan_0c~~age_0c                  2.3850  0.7879 20000   0.8483    3.9301
## coplan_0c~~bmi_0c                 -1.3462  0.5026 20000  -2.3214   -0.3583
## coplan_0c~~painvas_0c              0.1530  0.2045 20000  -0.2494    0.5455
## coplan_0c~~poshee_0c               0.1967  0.0889 20000   0.0232    0.3715
## coplan_0c~~swesourceNA_0c         -0.1307  0.1216 20000  -0.3677    0.1088
## coplan_0c~~fam3_0                  0.0230  0.0392 20000  -0.0534    0.0998
## muskeltraint_0c~~HIE               0.5372  0.8880 20000  -1.1927    2.2918
## muskeltraint_0c~~LIE              -0.2575  0.8884 20000  -1.9906    1.4942
## muskeltraint_0c~~sex_0            -0.9157  0.9080 20000  -2.6848    0.8661
## muskeltraint_0c~~age_0c           11.6412 14.2864 20000 -16.4026   39.7869
## muskeltraint_0c~~bmi_0c          -24.2002  9.2837 20000 -42.5532   -6.0702
## muskeltraint_0c~~painvas_0c       -1.1041  3.7346 20000  -8.3847    6.3062
## muskeltraint_0c~~poshee_0c         2.1524  1.6486 20000  -1.1045    5.3598
## muskeltraint_0c~~swesourceNA_0c   -2.6178  2.2259 20000  -6.9845    1.7366
## muskeltraint_0c~~fam3_0            1.0730  0.7225 20000  -0.3437    2.4855
## HIE~~LIE                          -0.1144  0.0161 20000  -0.1462   -0.0831
## HIE~~sex_0                        -0.0010  0.0148 20000  -0.0298    0.0279
## HIE~~age_0c                        0.1878  0.2297 20000  -0.2664    0.6344
## HIE~~bmi_0c                        0.3432  0.1506 20000   0.0458    0.6399
## HIE~~painvas_0c                   -0.0226  0.0611 20000  -0.1433    0.0978
## HIE~~poshee_0c                     0.0445  0.0267 20000  -0.0075    0.0970
## HIE~~swesourceNA_0c               -0.0364  0.0365 20000  -0.1079    0.0345
## HIE~~fam3_0                        0.0021  0.0117 20000  -0.0212    0.0250
## LIE~~sex_0                         0.0016  0.0148 20000  -0.0276    0.0312
## LIE~~age_0c                       -0.2255  0.2331 20000  -0.6813    0.2363
## LIE~~bmi_0c                       -0.3705  0.1513 20000  -0.6668   -0.0740
## LIE~~painvas_0c                    0.0342  0.0615 20000  -0.0865    0.1560
## LIE~~poshee_0c                    -0.0399  0.0265 20000  -0.0918    0.0114
## LIE~~swesourceNA_0c                0.0099  0.0363 20000  -0.0614    0.0807
## LIE~~fam3_0                       -0.0064  0.0117 20000  -0.0290    0.0167
## sex_0~~age_0c                      0.0533  0.2376 20000  -0.4123    0.5240
## sex_0~~bmi_0c                     -0.1011  0.1501 20000  -0.3947    0.1928
## sex_0~~painvas_0c                 -0.0066  0.0624 20000  -0.1307    0.1148
## sex_0~~poshee_0c                  -0.0339  0.0275 20000  -0.0887    0.0199
## sex_0~~swesourceNA_0c             -0.0742  0.0372 20000  -0.1467   -0.0013
## sex_0~~fam3_0                     -0.0253  0.0122 20000  -0.0491   -0.0016
## age_0c~~bmi_0c                    -1.6174  2.3986 20000  -6.3555    3.0622
## age_0c~~painvas_0c                -2.7480  0.9998 20000  -4.6992   -0.7873
## age_0c~~poshee_0c                 -1.3011  0.4310 20000  -2.1524   -0.4687
## age_0c~~swesourceNA_0c            -1.0936  0.5819 20000  -2.2452    0.0447
## age_0c~~fam3_0                    -0.0283  0.1879 20000  -0.3893    0.3413
## bmi_0c~~painvas_0c                 1.8405  0.6446 20000   0.5802    3.0989
## bmi_0c~~poshee_0c                  0.0724  0.2722 20000  -0.4596    0.6063
## bmi_0c~~swesourceNA_0c             0.6372  0.3734 20000  -0.0896    1.3781
## bmi_0c~~fam3_0                     0.0556  0.1206 20000  -0.1791    0.2959
## painvas_0c~~poshee_0c             -0.0256  0.1195 20000  -0.2594    0.2049
## painvas_0c~~swesourceNA_0c         0.3476  0.1543 20000   0.0468    0.6468
## painvas_0c~~fam3_0                 0.0629  0.0511 20000  -0.0360    0.1638
## poshee_0c~~swesourceNA_0c         -0.1245  0.0664 20000  -0.2542    0.0077
## poshee_0c~~fam3_0                  0.0027  0.0217 20000  -0.0399    0.0449
## swesourceNA_0c~~fam3_0             0.0065  0.0292 20000  -0.0502    0.0643
## coplan_18c~~coplan_18c             1.5955  0.1870 20000   1.2274    1.9592
## muskeltraint_24~~muskeltraint_24 515.5765 55.9595 20000 407.3775  626.2413
## groupfu~~groupfu                   0.2499  0.0226 20000   0.2053    0.2945
## coplan_0c~~coplan_0c               2.4827  0.2274 20000   2.0395    2.9300
## muskeltraint_0c~~muskeltraint_0c 850.7023 77.2484 20000 701.4063 1003.0272
## HIE~~HIE                           0.2231  0.0204 20000   0.1830    0.2634
## LIE~~LIE                           0.2245  0.0204 20000   0.1848    0.2646
## sex_0~~sex_0                       0.2340  0.0211 20000   0.1924    0.2752
## age_0c~~age_0c                    57.7334  5.2337 20000  47.4649   68.0357
## bmi_0c~~bmi_0c                    23.6379  2.1593 20000  19.4410   27.9404
## painvas_0c~~painvas_0c             3.9709  0.3669 20000   3.2537    4.6838
## poshee_0c~~poshee_0c               0.7571  0.0690 20000   0.6221    0.8916
## swesourceNA_0c~~swesourceNA_0c     1.3485  0.1242 20000   1.1069    1.5887
## fam3_0~~fam3_0                     0.1470  0.0135 20000   0.1205    0.1732
## coplan_18c~1                      -0.2757  0.2262 20000  -0.7148    0.1678
## muskeltraint_24~1                 22.8153  3.9256 20000  15.0535   30.5002
## groupfu~1                          0.5104  0.0322 20000   0.4474    0.5740
## coplan_0c~1                        0.0003  0.1018 20000  -0.2001    0.1990
## muskeltraint_0c~1                  0.0000  1.8678 20000  -3.6707    3.6051
## HIE~1                              0.3361  0.0303 20000   0.2767    0.3957
## LIE~1                              0.3402  0.0303 20000   0.2804    0.4000
## sex_0~1                            0.3734  0.0310 20000   0.3122    0.4339
## age_0c~1                           0.0000  0.4881 20000  -0.9501    0.9637
## bmi_0c~1                           0.0000  0.3144 20000  -0.6185    0.6185
## painvas_0c~1                      -0.0100  0.1296 20000  -0.2630    0.2483
## poshee_0c~1                        0.0011  0.0561 20000  -0.1079    0.1106
## swesourceNA_0c~1                  -0.0037  0.0765 20000  -0.1552    0.1435
## fam3_0~1                           0.1790  0.0248 20000   0.1301    0.2276
## ind                               -0.3421  0.6136 20000  -1.7945    0.7405
## total                              3.9055  3.6105 20000  -3.2449   11.0471

Maintenance Self-Efficacy at 12 Months

model <- '
# Direct Effects
aufswe_12c ~ a1*groupfu + a2*aufswe_0c + a3*muskeltraint_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
muskeltraint_24 ~ c1*groupfu + c2*aufswe_0c + c3*muskeltraint_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*aufswe_12c

# Covariances
groupfu ~~ aufswe_0c + muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
aufswe_0c ~~ muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
muskeltraint_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 403 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        11
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   aufswe_12c ~                                                           
##     groupfu   (a1)    -0.201    0.158   -1.277    0.201   -0.510    0.108
##     aufsw_0c  (a2)     0.117    0.069    1.704    0.088   -0.018    0.252
##     mskltr_0  (a3)    -0.004    0.004   -1.138    0.255   -0.011    0.003
##     HIE       (a4)    -0.338    0.188   -1.800    0.072   -0.706    0.030
##     LIE       (a5)    -0.036    0.190   -0.189    0.850   -0.409    0.337
##     sex_0     (a6)    -0.100    0.160   -0.624    0.533   -0.413    0.213
##     age_0c    (a7)     0.001    0.011    0.109    0.913   -0.020    0.023
##     bmi_0c    (a8)     0.009    0.017    0.524    0.601   -0.024    0.041
##     panvs_0c  (a9)    -0.086    0.041   -2.079    0.038   -0.167   -0.005
##     poshe_0c (a10)     0.139    0.093    1.485    0.138   -0.044    0.322
##     swsrNA_0 (a11)    -0.153    0.079   -1.926    0.054   -0.308    0.003
##     fam3_0   (a12)    -0.087    0.201   -0.433    0.665   -0.482    0.308
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     4.762    3.612    1.319    0.187   -2.317   11.841
##     aufsw_0c  (c2)     2.702    1.585    1.705    0.088   -0.405    5.810
##     mskltr_0  (c3)     0.449    0.075    5.979    0.000    0.302    0.597
##     HIE       (c4)    -4.807    4.411   -1.090    0.276  -13.452    3.838
##     LIE       (c5)     0.069    4.401    0.016    0.988   -8.556    8.694
##     sex_0     (c6)    -1.633    3.677   -0.444    0.657   -8.840    5.575
##     age_0c    (c7)     0.971    0.254    3.828    0.000    0.474    1.469
##     bmi_0c    (c8)    -0.172    0.384   -0.448    0.654   -0.926    0.581
##     panvs_0c  (c9)     2.457    0.967    2.541    0.011    0.562    4.352
##     poshe_0c (c10)     2.405    2.113    1.138    0.255   -1.737    6.547
##     swsrNA_0 (c11)    -2.515    1.731   -1.453    0.146   -5.908    0.879
##     fam3_0   (c12)    -2.145    4.786   -0.448    0.654  -11.525    7.234
##     afsw_12c  (b1)     0.691    1.913    0.361    0.718   -3.059    4.441
## 
## Covariances:
##                      Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                              
##     aufswe_0c          -0.066    0.038   -1.767    0.077   -0.140    0.007
##     muskeltrant_0c     -1.338    0.943   -1.418    0.156   -3.186    0.511
##     HIE                 0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE                -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0               0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c             -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c             -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c         -0.154    0.066   -2.357    0.018   -0.283   -0.026
##     poshee_0c           0.004    0.028    0.148    0.882   -0.051    0.059
##     swesourceNA_0c      0.067    0.038    1.753    0.080   -0.008    0.143
##     fam3_0             -0.033    0.013   -2.609    0.009   -0.057   -0.008
##   aufswe_0c ~~                                                            
##     muskeltrant_0c      4.183    2.197    1.903    0.057   -0.124    8.490
##     HIE                 0.055    0.035    1.540    0.124   -0.015    0.124
##     LIE                 0.007    0.035    0.206    0.836   -0.062    0.077
##     sex_0              -0.027    0.036   -0.746    0.455   -0.098    0.044
##     age_0c             -0.053    0.568   -0.093    0.926   -1.166    1.061
##     bmi_0c             -0.409    0.364   -1.122    0.262   -1.123    0.305
##     painvas_0c         -0.102    0.150   -0.678    0.498   -0.396    0.192
##     poshee_0c           0.219    0.067    3.279    0.001    0.088    0.350
##     swesourceNA_0c     -0.069    0.088   -0.781    0.435   -0.242    0.104
##     fam3_0              0.025    0.029    0.851    0.395   -0.032    0.081
##   muskeltraint_0c ~~                                                      
##     HIE                 0.537    0.888    0.605    0.545   -1.204    2.278
##     LIE                -0.258    0.890   -0.289    0.772   -2.003    1.487
##     sex_0              -0.916    0.911   -1.005    0.315   -2.701    0.869
##     age_0c             11.641   14.295    0.814    0.415  -16.377   39.659
##     bmi_0c            -24.200    9.267   -2.612    0.009  -42.362   -6.038
##     painvas_0c         -1.184    3.766   -0.314    0.753   -8.566    6.198
##     poshee_0c           2.151    1.642    1.310    0.190   -1.067    5.370
##     swesourceNA_0c     -2.617    2.216   -1.181    0.238   -6.960    1.727
##     fam3_0              1.074    0.724    1.483    0.138   -0.345    2.494
##   HIE ~~                                                                  
##     LIE                -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0              -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c              0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c              0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c         -0.023    0.061   -0.381    0.703   -0.143    0.097
##     poshee_0c           0.045    0.027    1.699    0.089   -0.007    0.098
##     swesourceNA_0c     -0.038    0.036   -1.052    0.293   -0.109    0.033
##     fam3_0              0.002    0.012    0.181    0.856   -0.021    0.025
##   LIE ~~                                                                  
##     sex_0               0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c             -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c             -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c          0.036    0.062    0.580    0.562   -0.085    0.157
##     poshee_0c          -0.040    0.027   -1.513    0.130   -0.093    0.012
##     swesourceNA_0c      0.011    0.036    0.312    0.755   -0.060    0.082
##     fam3_0             -0.006    0.012   -0.550    0.583   -0.030    0.017
##   sex_0 ~~                                                                
##     age_0c              0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c             -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c         -0.007    0.063   -0.104    0.917   -0.129    0.116
##     poshee_0c          -0.034    0.027   -1.264    0.206   -0.088    0.019
##     swesourceNA_0c     -0.074    0.037   -1.981    0.048   -0.147   -0.001
##     fam3_0             -0.025    0.012   -2.096    0.036   -0.049   -0.002
##   age_0c ~~                                                               
##     bmi_0c             -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c         -2.783    0.997   -2.792    0.005   -4.737   -0.829
##     poshee_0c          -1.297    0.434   -2.986    0.003   -2.149   -0.446
##     swesourceNA_0c     -1.105    0.581   -1.903    0.057   -2.244    0.033
##     fam3_0             -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                               
##     painvas_0c          1.828    0.643    2.844    0.004    0.568    3.087
##     poshee_0c           0.085    0.274    0.310    0.756   -0.453    0.623
##     swesourceNA_0c      0.630    0.374    1.684    0.092   -0.103    1.364
##     fam3_0              0.055    0.121    0.457    0.648   -0.181    0.291
##   painvas_0c ~~                                                           
##     poshee_0c          -0.019    0.120   -0.158    0.874   -0.255    0.217
##     swesourceNA_0c      0.360    0.153    2.344    0.019    0.059    0.661
##     fam3_0              0.061    0.051    1.208    0.227   -0.038    0.161
##   poshee_0c ~~                                                            
##     swesourceNA_0c     -0.125    0.067   -1.874    0.061   -0.256    0.006
##     fam3_0              0.002    0.022    0.112    0.910   -0.040    0.045
##   swesourceNA_0c ~~                                                       
##     fam3_0              0.005    0.029    0.185    0.853   -0.052    0.063
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_12c        0.249    0.163    1.523    0.128   -0.071    0.569
##    .muskeltrant_24   22.996    3.919    5.868    0.000   15.315   30.677
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     aufswe_0c         0.000    0.075    0.000    1.000   -0.147    0.147
##     muskeltrant_0c    0.000    1.879    0.000    1.000   -3.682    3.682
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c            0.000    0.489    0.000    1.000   -0.959    0.959
##     bmi_0c           -0.000    0.313   -0.000    1.000   -0.614    0.614
##     painvas_0c       -0.014    0.130   -0.107    0.915   -0.268    0.240
##     poshee_0c         0.003    0.056    0.046    0.963   -0.108    0.113
##     swesourceNA_0c   -0.004    0.076   -0.048    0.962   -0.153    0.146
##     fam3_0            0.179    0.025    7.227    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_12c        0.922    0.103    8.985    0.000    0.721    1.123
##    .muskeltrant_24  511.622   55.374    9.239    0.000  403.091  620.154
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     aufswe_0c         1.347    0.123   10.977    0.000    1.107    1.588
##     muskeltrant_0c  850.703   77.497   10.977    0.000  698.811 1002.594
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.973    0.366   10.846    0.000    3.255    4.690
##     poshee_0c         0.758    0.069   10.939    0.000    0.623    0.894
##     swesourceNA_0c    1.349    0.126   10.750    0.000    1.103    1.595
##     fam3_0            0.147    0.013   10.956    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.139    0.396   -0.352    0.725   -0.915    0.636
##     total             4.623    3.588    1.288    0.198   -2.409   11.655

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                -0.2014  0.1582 20000  -0.5111    0.1096
## a2                                 0.1172  0.0694 20000  -0.0204    0.2529
## a3                                -0.0042  0.0037 20000  -0.0114    0.0030
## a4                                -0.3379  0.1888 20000  -0.7107    0.0303
## a5                                -0.0359  0.1916 20000  -0.4142    0.3381
## a6                                -0.0996  0.1600 20000  -0.4092    0.2189
## a7                                 0.0012  0.0110 20000  -0.0205    0.0227
## a8                                 0.0087  0.0166 20000  -0.0235    0.0413
## a9                                -0.0859  0.0413 20000  -0.1674   -0.0072
## a10                                0.1386  0.0932 20000  -0.0469    0.3197
## a11                               -0.1527  0.0796 20000  -0.3095    0.0021
## a12                               -0.0871  0.2015 20000  -0.4804    0.3088
## c1                                 4.7620  3.5877 20000  -2.3499   11.6505
## c2                                 2.7025  1.5892 20000  -0.4100    5.7872
## c3                                 0.4494  0.0755 20000   0.3011    0.5977
## c4                                -4.8069  4.3955 20000 -13.5530    3.7052
## c5                                 0.0687  4.3971 20000  -8.5365    8.7059
## c6                                -1.6326  3.6307 20000  -8.6633    5.5165
## c7                                 0.9713  0.2555 20000   0.4779    1.4737
## c8                                -0.1722  0.3820 20000  -0.9225    0.5727
## c9                                 2.4570  0.9661 20000   0.5705    4.3517
## c10                                2.4048  2.1272 20000  -1.7565    6.5666
## c11                               -2.5147  1.7308 20000  -5.8919    0.9065
## c12                               -2.1454  4.7436 20000 -11.4467    6.9449
## b1                                 0.6911  1.9015 20000  -3.0456    4.3478
## groupfu~~aufswe_0c                -0.0665  0.0377 20000  -0.1400    0.0076
## groupfu~~muskeltraint_0c          -1.3377  0.9436 20000  -3.1691    0.5057
## groupfu~~HIE                       0.0069  0.0151 20000  -0.0229    0.0361
## groupfu~~LIE                      -0.0035  0.0152 20000  -0.0333    0.0262
## groupfu~~sex_0                     0.0003  0.0156 20000  -0.0299    0.0306
## groupfu~~age_0c                   -0.0706  0.2443 20000  -0.5486    0.4127
## groupfu~~bmi_0c                   -0.0398  0.1569 20000  -0.3479    0.2647
## groupfu~~painvas_0c               -0.1545  0.0658 20000  -0.2862   -0.0276
## groupfu~~poshee_0c                 0.0042  0.0279 20000  -0.0506    0.0591
## groupfu~~swesourceNA_0c            0.0674  0.0382 20000  -0.0075    0.1420
## groupfu~~fam3_0                   -0.0327  0.0126 20000  -0.0575   -0.0081
## aufswe_0c~~muskeltraint_0c         4.1827  2.2062 20000  -0.1636    8.4867
## aufswe_0c~~HIE                     0.0547  0.0353 20000  -0.0136    0.1241
## aufswe_0c~~LIE                     0.0073  0.0353 20000  -0.0629    0.0750
## aufswe_0c~~sex_0                  -0.0270  0.0364 20000  -0.0975    0.0451
## aufswe_0c~~age_0c                 -0.0527  0.5697 20000  -1.1659    1.0597
## aufswe_0c~~bmi_0c                 -0.4089  0.3654 20000  -1.1294    0.3083
## aufswe_0c~~painvas_0c             -0.1017  0.1495 20000  -0.3935    0.1914
## aufswe_0c~~poshee_0c               0.2192  0.0671 20000   0.0892    0.3509
## aufswe_0c~~swesourceNA_0c         -0.0690  0.0884 20000  -0.2416    0.1058
## aufswe_0c~~fam3_0                  0.0245  0.0286 20000  -0.0320    0.0801
## muskeltraint_0c~~HIE               0.5372  0.8900 20000  -1.2074    2.2804
## muskeltraint_0c~~LIE              -0.2575  0.8900 20000  -1.9930    1.4992
## muskeltraint_0c~~sex_0            -0.9157  0.9104 20000  -2.7030    0.8618
## muskeltraint_0c~~age_0c           11.6412 14.2971 20000 -16.6539   39.6466
## muskeltraint_0c~~bmi_0c          -24.2002  9.2615 20000 -42.4420   -6.2123
## muskeltraint_0c~~painvas_0c       -1.1842  3.7959 20000  -8.7302    6.2363
## muskeltraint_0c~~poshee_0c         2.1512  1.6511 20000  -1.1097    5.3507
## muskeltraint_0c~~swesourceNA_0c   -2.6165  2.2317 20000  -7.0235    1.7561
## muskeltraint_0c~~fam3_0            1.0744  0.7278 20000  -0.3414    2.4847
## HIE~~LIE                          -0.1144  0.0162 20000  -0.1456   -0.0828
## HIE~~sex_0                        -0.0010  0.0147 20000  -0.0295    0.0277
## HIE~~age_0c                        0.1878  0.2334 20000  -0.2669    0.6407
## HIE~~bmi_0c                        0.3432  0.1498 20000   0.0494    0.6341
## HIE~~painvas_0c                   -0.0233  0.0609 20000  -0.1424    0.0941
## HIE~~poshee_0c                     0.0455  0.0268 20000  -0.0066    0.0978
## HIE~~swesourceNA_0c               -0.0382  0.0364 20000  -0.1097    0.0327
## HIE~~fam3_0                        0.0021  0.0117 20000  -0.0211    0.0248
## LIE~~sex_0                         0.0016  0.0148 20000  -0.0273    0.0306
## LIE~~age_0c                       -0.2255  0.2343 20000  -0.6841    0.2378
## LIE~~bmi_0c                       -0.3705  0.1498 20000  -0.6614   -0.0775
## LIE~~painvas_0c                    0.0358  0.0614 20000  -0.0845    0.1562
## LIE~~poshee_0c                    -0.0405  0.0266 20000  -0.0926    0.0109
## LIE~~swesourceNA_0c                0.0113  0.0362 20000  -0.0595    0.0829
## LIE~~fam3_0                       -0.0065  0.0117 20000  -0.0294    0.0166
## sex_0~~age_0c                      0.0533  0.2357 20000  -0.4091    0.5109
## sex_0~~bmi_0c                     -0.1011  0.1524 20000  -0.3981    0.1979
## sex_0~~painvas_0c                 -0.0065  0.0627 20000  -0.1279    0.1172
## sex_0~~poshee_0c                  -0.0345  0.0273 20000  -0.0875    0.0197
## sex_0~~swesourceNA_0c             -0.0736  0.0372 20000  -0.1465   -0.0021
## sex_0~~fam3_0                     -0.0253  0.0121 20000  -0.0492   -0.0016
## age_0c~~bmi_0c                    -1.6174  2.3953 20000  -6.2906    3.0942
## age_0c~~painvas_0c                -2.7832  0.9918 20000  -4.7384   -0.8456
## age_0c~~poshee_0c                 -1.2974  0.4334 20000  -2.1457   -0.4396
## age_0c~~swesourceNA_0c            -1.1052  0.5820 20000  -2.2327    0.0379
## age_0c~~fam3_0                    -0.0283  0.1889 20000  -0.3996    0.3420
## bmi_0c~~painvas_0c                 1.8275  0.6405 20000   0.5710    3.0982
## bmi_0c~~poshee_0c                  0.0852  0.2732 20000  -0.4546    0.6126
## bmi_0c~~swesourceNA_0c             0.6305  0.3729 20000  -0.1029    1.3668
## bmi_0c~~fam3_0                     0.0551  0.1220 20000  -0.1846    0.2937
## painvas_0c~~poshee_0c             -0.0190  0.1201 20000  -0.2548    0.2137
## painvas_0c~~swesourceNA_0c         0.3598  0.1556 20000   0.0550    0.6644
## painvas_0c~~fam3_0                 0.0613  0.0506 20000  -0.0375    0.1599
## poshee_0c~~swesourceNA_0c         -0.1251  0.0666 20000  -0.2570    0.0040
## poshee_0c~~fam3_0                  0.0024  0.0215 20000  -0.0395    0.0448
## swesourceNA_0c~~fam3_0             0.0054  0.0292 20000  -0.0524    0.0620
## aufswe_12c~~aufswe_12c             0.9217  0.1025 20000   0.7204    1.1250
## muskeltraint_24~~muskeltraint_24 511.6222 55.4786 20000 401.9127  618.8927
## groupfu~~groupfu                   0.2499  0.0227 20000   0.2056    0.2949
## aufswe_0c~~aufswe_0c               1.3474  0.1225 20000   1.1068    1.5879
## muskeltraint_0c~~muskeltraint_0c 850.7025 77.2492 20000 698.5260 1000.0857
## HIE~~HIE                           0.2231  0.0203 20000   0.1828    0.2627
## LIE~~LIE                           0.2245  0.0207 20000   0.1836    0.2644
## sex_0~~sex_0                       0.2340  0.0213 20000   0.1923    0.2754
## age_0c~~age_0c                    57.7334  5.2357 20000  47.4895   68.0387
## bmi_0c~~bmi_0c                    23.6379  2.1627 20000  19.4136   27.8937
## painvas_0c~~painvas_0c             3.9726  0.3672 20000   3.2586    4.6960
## poshee_0c~~poshee_0c               0.7584  0.0695 20000   0.6222    0.8933
## swesourceNA_0c~~swesourceNA_0c     1.3491  0.1253 20000   1.1004    1.5940
## fam3_0~~fam3_0                     0.1470  0.0135 20000   0.1207    0.1735
## aufswe_12c~1                       0.2491  0.1642 20000  -0.0756    0.5720
## muskeltraint_24~1                 22.9962  3.9133 20000  15.4841   30.7322
## groupfu~1                          0.5104  0.0323 20000   0.4475    0.5730
## aufswe_0c~1                        0.0000  0.0749 20000  -0.1469    0.1456
## muskeltraint_0c~1                  0.0000  1.8880 20000  -3.6628    3.7564
## HIE~1                              0.3361  0.0302 20000   0.2773    0.3958
## LIE~1                              0.3402  0.0305 20000   0.2808    0.4012
## sex_0~1                            0.3734  0.0311 20000   0.3122    0.4342
## age_0c~1                           0.0000  0.4882 20000  -0.9619    0.9480
## bmi_0c~1                           0.0000  0.3119 20000  -0.6051    0.6150
## painvas_0c~1                      -0.0139  0.1294 20000  -0.2680    0.2409
## poshee_0c~1                        0.0026  0.0563 20000  -0.1086    0.1123
## swesourceNA_0c~1                  -0.0037  0.0763 20000  -0.1532    0.1447
## fam3_0~1                           0.1789  0.0247 20000   0.1306    0.2278
## ind                               -0.1392  0.4984 20000  -1.2819    0.8505
## total                              4.6228  3.5745 20000  -2.4922   11.5488

Maintenance Self-Efficacy at 18 Months

model <- '
# Direct Effects
aufswe_18c ~ a1*groupfu + a2*aufswe_0c + a3*muskeltraint_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
muskeltraint_24 ~ c1*groupfu + c2*aufswe_0c + c3*muskeltraint_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*aufswe_18c

# Covariances
groupfu ~~ aufswe_0c + muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
aufswe_0c ~~ muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
muskeltraint_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 407 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        12
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   aufswe_18c ~                                                           
##     groupfu   (a1)    -0.039    0.172   -0.227    0.820   -0.376    0.298
##     aufsw_0c  (a2)     0.216    0.074    2.934    0.003    0.072    0.361
##     mskltr_0  (a3)     0.002    0.004    0.427    0.669   -0.006    0.010
##     HIE       (a4)    -0.119    0.205   -0.581    0.561   -0.522    0.283
##     LIE       (a5)    -0.034    0.209   -0.162    0.871   -0.444    0.376
##     sex_0     (a6)    -0.128    0.176   -0.731    0.465   -0.472    0.216
##     age_0c    (a7)     0.013    0.012    1.072    0.284   -0.011    0.037
##     bmi_0c    (a8)    -0.019    0.018   -1.050    0.294   -0.055    0.017
##     panvs_0c  (a9)     0.008    0.048    0.158    0.875   -0.086    0.102
##     poshe_0c (a10)     0.207    0.099    2.085    0.037    0.012    0.401
##     swsrNA_0 (a11)    -0.089    0.087   -1.024    0.306   -0.259    0.081
##     fam3_0   (a12)    -0.099    0.219   -0.451    0.652   -0.528    0.331
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     4.579    3.587    1.276    0.202   -2.452   11.610
##     aufsw_0c  (c2)     2.938    1.616    1.818    0.069   -0.229    6.105
##     mskltr_0  (c3)     0.447    0.074    5.994    0.000    0.301    0.592
##     HIE       (c4)    -5.138    4.350   -1.181    0.238  -13.664    3.389
##     LIE       (c5)    -0.025    4.401   -0.006    0.995   -8.652    8.602
##     sex_0     (c6)    -1.809    3.679   -0.492    0.623   -9.020    5.402
##     age_0c    (c7)     0.978    0.255    3.840    0.000    0.479    1.478
##     bmi_0c    (c8)    -0.177    0.386   -0.458    0.647   -0.934    0.580
##     panvs_0c  (c9)     2.394    0.955    2.506    0.012    0.522    4.265
##     poshe_0c (c10)     2.612    2.127    1.228    0.219   -1.556    6.780
##     swsrNA_0 (c11)    -2.664    1.714   -1.554    0.120   -6.023    0.695
##     fam3_0   (c12)    -2.327    4.776   -0.487    0.626  -11.689    7.034
##     afsw_18c  (b1)    -0.603    1.833   -0.329    0.742   -4.195    2.989
## 
## Covariances:
##                      Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                              
##     aufswe_0c          -0.066    0.038   -1.767    0.077   -0.140    0.007
##     muskeltrant_0c     -1.338    0.943   -1.418    0.156   -3.186    0.511
##     HIE                 0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE                -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0               0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c             -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c             -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c         -0.153    0.066   -2.339    0.019   -0.282   -0.025
##     poshee_0c           0.004    0.028    0.150    0.881   -0.051    0.059
##     swesourceNA_0c      0.069    0.038    1.781    0.075   -0.007    0.144
##     fam3_0             -0.033    0.013   -2.609    0.009   -0.057   -0.008
##   aufswe_0c ~~                                                            
##     muskeltrant_0c      4.183    2.197    1.903    0.057   -0.124    8.490
##     HIE                 0.055    0.035    1.540    0.124   -0.015    0.124
##     LIE                 0.007    0.035    0.206    0.836   -0.062    0.077
##     sex_0              -0.027    0.036   -0.746    0.455   -0.098    0.044
##     age_0c             -0.053    0.568   -0.093    0.926   -1.166    1.061
##     bmi_0c             -0.409    0.364   -1.122    0.262   -1.123    0.305
##     painvas_0c         -0.104    0.150   -0.691    0.489   -0.397    0.190
##     poshee_0c           0.219    0.067    3.281    0.001    0.088    0.350
##     swesourceNA_0c     -0.068    0.088   -0.765    0.444   -0.241    0.106
##     fam3_0              0.025    0.029    0.851    0.395   -0.032    0.081
##   muskeltraint_0c ~~                                                      
##     HIE                 0.537    0.888    0.605    0.545   -1.204    2.278
##     LIE                -0.258    0.890   -0.289    0.772   -2.003    1.487
##     sex_0              -0.916    0.911   -1.005    0.315   -2.701    0.869
##     age_0c             11.641   14.295    0.814    0.415  -16.377   39.659
##     bmi_0c            -24.200    9.267   -2.612    0.009  -42.362   -6.038
##     painvas_0c         -1.158    3.767   -0.308    0.758   -8.541    6.224
##     poshee_0c           2.151    1.642    1.310    0.190   -1.068    5.370
##     swesourceNA_0c     -2.604    2.217   -1.175    0.240   -6.949    1.740
##     fam3_0              1.074    0.724    1.483    0.138   -0.345    2.494
##   HIE ~~                                                                  
##     LIE                -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0              -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c              0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c              0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c         -0.024    0.061   -0.397    0.691   -0.144    0.096
##     poshee_0c           0.046    0.027    1.702    0.089   -0.007    0.098
##     swesourceNA_0c     -0.037    0.036   -1.009    0.313   -0.108    0.035
##     fam3_0              0.002    0.012    0.181    0.856   -0.021    0.025
##   LIE ~~                                                                  
##     sex_0               0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c             -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c             -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c          0.037    0.062    0.593    0.553   -0.084    0.157
##     poshee_0c          -0.040    0.027   -1.515    0.130   -0.093    0.012
##     swesourceNA_0c      0.010    0.036    0.280    0.779   -0.061    0.081
##     fam3_0             -0.006    0.012   -0.550    0.583   -0.030    0.017
##   sex_0 ~~                                                                
##     age_0c              0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c             -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c         -0.007    0.063   -0.107    0.915   -0.130    0.116
##     poshee_0c          -0.034    0.027   -1.265    0.206   -0.088    0.019
##     swesourceNA_0c     -0.074    0.037   -1.999    0.046   -0.147   -0.001
##     fam3_0             -0.025    0.012   -2.096    0.036   -0.049   -0.002
##   age_0c ~~                                                               
##     bmi_0c             -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c         -2.782    0.997   -2.791    0.005   -4.736   -0.828
##     poshee_0c          -1.297    0.434   -2.986    0.003   -2.149   -0.446
##     swesourceNA_0c     -1.100    0.581   -1.895    0.058   -2.239    0.038
##     fam3_0             -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                               
##     painvas_0c          1.814    0.643    2.822    0.005    0.554    3.073
##     poshee_0c           0.086    0.274    0.314    0.754   -0.452    0.624
##     swesourceNA_0c      0.633    0.375    1.690    0.091   -0.101    1.367
##     fam3_0              0.055    0.121    0.457    0.648   -0.181    0.291
##   painvas_0c ~~                                                           
##     poshee_0c          -0.020    0.121   -0.162    0.872   -0.256    0.217
##     swesourceNA_0c      0.355    0.153    2.315    0.021    0.054    0.656
##     fam3_0              0.061    0.051    1.198    0.231   -0.039    0.160
##   poshee_0c ~~                                                            
##     swesourceNA_0c     -0.124    0.067   -1.859    0.063   -0.255    0.007
##     fam3_0              0.002    0.022    0.112    0.911   -0.040    0.045
##   swesourceNA_0c ~~                                                       
##     fam3_0              0.006    0.029    0.192    0.847   -0.052    0.063
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_18c        0.152    0.181    0.838    0.402   -0.203    0.506
##    .muskeltrant_24   23.308    3.882    6.004    0.000   15.700   30.917
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     aufswe_0c         0.000    0.075    0.000    1.000   -0.147    0.147
##     muskeltrant_0c    0.000    1.879    0.000    1.000   -3.682    3.682
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c            0.000    0.489    0.000    1.000   -0.959    0.959
##     bmi_0c           -0.000    0.313   -0.000    1.000   -0.614    0.614
##     painvas_0c       -0.013    0.130   -0.098    0.922   -0.267    0.242
##     poshee_0c         0.003    0.056    0.048    0.962   -0.107    0.113
##     swesourceNA_0c   -0.002    0.076   -0.020    0.984   -0.151    0.148
##     fam3_0            0.179    0.025    7.227    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .aufswe_18c        1.036    0.119    8.712    0.000    0.803    1.269
##    .muskeltrant_24  511.760   55.392    9.239    0.000  403.194  620.327
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     aufswe_0c         1.347    0.123   10.977    0.000    1.107    1.588
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.970    0.366   10.850    0.000    3.253    4.687
##     poshee_0c         0.759    0.069   10.938    0.000    0.623    0.894
##     swesourceNA_0c    1.349    0.126   10.749    0.000    1.103    1.595
##     fam3_0            0.147    0.013   10.956    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.024    0.130    0.181    0.856   -0.231    0.278
##     total             4.603    3.588    1.283    0.200   -2.429   11.634

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                -0.0390  0.1720 20000  -0.3772    0.2999
## a2                                 0.2162  0.0733 20000   0.0734    0.3582
## a3                                 0.0017  0.0041 20000  -0.0061    0.0097
## a4                                -0.1193  0.2060 20000  -0.5232    0.2813
## a5                                -0.0339  0.2116 20000  -0.4495    0.3829
## a6                                -0.1284  0.1751 20000  -0.4699    0.2140
## a7                                 0.0132  0.0123 20000  -0.0108    0.0378
## a8                                -0.0192  0.0185 20000  -0.0553    0.0169
## a9                                 0.0076  0.0480 20000  -0.0869    0.1011
## a10                                0.2066  0.0995 20000   0.0122    0.4025
## a11                               -0.0888  0.0869 20000  -0.2572    0.0796
## a12                               -0.0988  0.2197 20000  -0.5294    0.3324
## c1                                 4.5791  3.5781 20000  -2.4398   11.6006
## c2                                 2.9379  1.6244 20000  -0.2495    6.0861
## c3                                 0.4465  0.0755 20000   0.2956    0.5938
## c4                                -5.1377  4.3289 20000 -13.8269    3.2010
## c5                                -0.0250  4.3814 20000  -8.7130    8.5320
## c6                                -1.8091  3.6225 20000  -8.8019    5.3900
## c7                                 0.9785  0.2548 20000   0.4810    1.4781
## c8                                -0.1770  0.3840 20000  -0.9287    0.5802
## c9                                 2.3936  0.9556 20000   0.5351    4.2718
## c10                                2.6118  2.1326 20000  -1.5189    6.8196
## c11                               -2.6639  1.7191 20000  -6.0138    0.7206
## c12                               -2.3274  4.7428 20000 -11.6956    6.8965
## b1                                -0.6029  1.8236 20000  -4.1796    2.9374
## groupfu~~aufswe_0c                -0.0665  0.0377 20000  -0.1404    0.0073
## groupfu~~muskeltraint_0c          -1.3377  0.9396 20000  -3.1856    0.4903
## groupfu~~HIE                       0.0069  0.0151 20000  -0.0227    0.0366
## groupfu~~LIE                      -0.0035  0.0151 20000  -0.0329    0.0260
## groupfu~~sex_0                     0.0003  0.0156 20000  -0.0299    0.0307
## groupfu~~age_0c                   -0.0706  0.2431 20000  -0.5459    0.4039
## groupfu~~bmi_0c                   -0.0398  0.1562 20000  -0.3445    0.2691
## groupfu~~painvas_0c               -0.1533  0.0656 20000  -0.2825   -0.0266
## groupfu~~poshee_0c                 0.0042  0.0280 20000  -0.0509    0.0596
## groupfu~~swesourceNA_0c            0.0685  0.0383 20000  -0.0058    0.1442
## groupfu~~fam3_0                   -0.0327  0.0127 20000  -0.0575   -0.0077
## aufswe_0c~~muskeltraint_0c         4.1827  2.2136 20000  -0.2271    8.5766
## aufswe_0c~~HIE                     0.0547  0.0354 20000  -0.0142    0.1240
## aufswe_0c~~LIE                     0.0073  0.0355 20000  -0.0622    0.0776
## aufswe_0c~~sex_0                  -0.0270  0.0363 20000  -0.0983    0.0445
## aufswe_0c~~age_0c                 -0.0527  0.5701 20000  -1.1789    1.0494
## aufswe_0c~~bmi_0c                 -0.4089  0.3634 20000  -1.1057    0.3100
## aufswe_0c~~painvas_0c             -0.1036  0.1504 20000  -0.3972    0.1938
## aufswe_0c~~poshee_0c               0.2194  0.0674 20000   0.0866    0.3511
## aufswe_0c~~swesourceNA_0c         -0.0676  0.0883 20000  -0.2405    0.1043
## aufswe_0c~~fam3_0                  0.0245  0.0289 20000  -0.0320    0.0817
## muskeltraint_0c~~HIE               0.5372  0.8893 20000  -1.1967    2.2824
## muskeltraint_0c~~LIE              -0.2575  0.8885 20000  -2.0078    1.4668
## muskeltraint_0c~~sex_0            -0.9157  0.9148 20000  -2.6778    0.8956
## muskeltraint_0c~~age_0c           11.6412 14.2980 20000 -16.2981   39.9958
## muskeltraint_0c~~bmi_0c          -24.2002  9.2582 20000 -42.1740   -6.0654
## muskeltraint_0c~~painvas_0c       -1.1583  3.7895 20000  -8.6170    6.1558
## muskeltraint_0c~~poshee_0c         2.1511  1.6529 20000  -1.0731    5.4404
## muskeltraint_0c~~swesourceNA_0c   -2.6042  2.2275 20000  -7.0219    1.7504
## muskeltraint_0c~~fam3_0            1.0743  0.7281 20000  -0.3326    2.4918
## HIE~~LIE                          -0.1144  0.0160 20000  -0.1455   -0.0825
## HIE~~sex_0                        -0.0010  0.0148 20000  -0.0302    0.0280
## HIE~~age_0c                        0.1878  0.2335 20000  -0.2660    0.6459
## HIE~~bmi_0c                        0.3432  0.1512 20000   0.0458    0.6382
## HIE~~painvas_0c                   -0.0243  0.0608 20000  -0.1415    0.0950
## HIE~~poshee_0c                     0.0455  0.0267 20000  -0.0068    0.0979
## HIE~~swesourceNA_0c               -0.0366  0.0363 20000  -0.1086    0.0346
## HIE~~fam3_0                        0.0021  0.0116 20000  -0.0206    0.0251
## LIE~~sex_0                         0.0016  0.0149 20000  -0.0280    0.0308
## LIE~~age_0c                       -0.2255  0.2355 20000  -0.6904    0.2377
## LIE~~bmi_0c                       -0.3705  0.1509 20000  -0.6636   -0.0733
## LIE~~painvas_0c                    0.0366  0.0614 20000  -0.0840    0.1556
## LIE~~poshee_0c                    -0.0405  0.0267 20000  -0.0933    0.0119
## LIE~~swesourceNA_0c                0.0101  0.0362 20000  -0.0607    0.0816
## LIE~~fam3_0                       -0.0065  0.0117 20000  -0.0299    0.0163
## sex_0~~age_0c                      0.0533  0.2376 20000  -0.4118    0.5231
## sex_0~~bmi_0c                     -0.1011  0.1526 20000  -0.4018    0.1956
## sex_0~~painvas_0c                 -0.0067  0.0624 20000  -0.1285    0.1146
## sex_0~~poshee_0c                  -0.0345  0.0272 20000  -0.0872    0.0190
## sex_0~~swesourceNA_0c             -0.0744  0.0374 20000  -0.1482   -0.0014
## sex_0~~fam3_0                     -0.0253  0.0121 20000  -0.0488   -0.0017
## age_0c~~bmi_0c                    -1.6174  2.4051 20000  -6.3742    3.0186
## age_0c~~painvas_0c                -2.7821  0.9956 20000  -4.7222   -0.8182
## age_0c~~poshee_0c                 -1.2971  0.4349 20000  -2.1411   -0.4409
## age_0c~~swesourceNA_0c            -1.1005  0.5831 20000  -2.2408    0.0509
## age_0c~~fam3_0                    -0.0283  0.1878 20000  -0.4001    0.3428
## bmi_0c~~painvas_0c                 1.8137  0.6445 20000   0.5377    3.0780
## bmi_0c~~poshee_0c                  0.0861  0.2720 20000  -0.4447    0.6209
## bmi_0c~~swesourceNA_0c             0.6330  0.3757 20000  -0.1071    1.3584
## bmi_0c~~fam3_0                     0.0551  0.1198 20000  -0.1817    0.2854
## painvas_0c~~poshee_0c             -0.0195  0.1204 20000  -0.2591    0.2140
## painvas_0c~~swesourceNA_0c         0.3551  0.1542 20000   0.0488    0.6557
## painvas_0c~~fam3_0                 0.0609  0.0504 20000  -0.0390    0.1594
## poshee_0c~~swesourceNA_0c         -0.1241  0.0665 20000  -0.2539    0.0061
## poshee_0c~~fam3_0                  0.0024  0.0213 20000  -0.0403    0.0439
## swesourceNA_0c~~fam3_0             0.0056  0.0292 20000  -0.0517    0.0628
## aufswe_18c~~aufswe_18c             1.0360  0.1190 20000   0.8017    1.2683
## muskeltraint_24~~muskeltraint_24 511.7603 55.4966 20000 402.0146  619.0659
## groupfu~~groupfu                   0.2499  0.0227 20000   0.2054    0.2945
## aufswe_0c~~aufswe_0c               1.3474  0.1226 20000   1.1090    1.5878
## muskeltraint_0c~~muskeltraint_0c 850.7023 77.2492 20000 701.3093 1002.8764
## HIE~~HIE                           0.2231  0.0201 20000   0.1832    0.2622
## LIE~~LIE                           0.2245  0.0205 20000   0.1840    0.2649
## sex_0~~sex_0                       0.2340  0.0212 20000   0.1926    0.2752
## age_0c~~age_0c                    57.7334  5.2349 20000  47.4687   67.9431
## bmi_0c~~bmi_0c                    23.6379  2.1630 20000  19.3638   27.7770
## painvas_0c~~painvas_0c             3.9702  0.3672 20000   3.2449    4.6860
## poshee_0c~~poshee_0c               0.7585  0.0692 20000   0.6218    0.8941
## swesourceNA_0c~~swesourceNA_0c     1.3491  0.1271 20000   1.1030    1.5999
## fam3_0~~fam3_0                     0.1470  0.0134 20000   0.1203    0.1727
## aufswe_18c~1                       0.1517  0.1821 20000  -0.2024    0.5118
## muskeltraint_24~1                 23.3082  3.8860 20000  15.6244   30.9801
## groupfu~1                          0.5104  0.0321 20000   0.4478    0.5737
## aufswe_0c~1                        0.0000  0.0743 20000  -0.1472    0.1460
## muskeltraint_0c~1                  0.0000  1.8880 20000  -3.6628    3.7441
## HIE~1                              0.3361  0.0302 20000   0.2764    0.3957
## LIE~1                              0.3402  0.0304 20000   0.2799    0.4000
## sex_0~1                            0.3734  0.0312 20000   0.3127    0.4353
## age_0c~1                           0.0000  0.4880 20000  -0.9581    0.9445
## bmi_0c~1                           0.0000  0.3123 20000  -0.6124    0.6102
## painvas_0c~1                      -0.0127  0.1302 20000  -0.2698    0.2390
## poshee_0c~1                        0.0027  0.0563 20000  -0.1070    0.1132
## swesourceNA_0c~1                  -0.0015  0.0768 20000  -0.1521    0.1490
## fam3_0~1                           0.1789  0.0248 20000   0.1310    0.2282
## ind                                0.0235  0.3383 20000  -0.6034    0.8473
## total                              4.6026  3.5930 20000  -2.4158   11.6458

Recovery Self-Efficacy at 12 Months

model <- '
# Direct Effects
wieswe_12c ~ a1*groupfu + a2*wieswe_0c + a3*muskeltraint_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
muskeltraint_24 ~ c1*groupfu + c2*wieswe_0c + c3*muskeltraint_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*wieswe_12c

# Covariances
groupfu ~~ wieswe_0c + muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
wieswe_0c ~~ muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
muskeltraint_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 400 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        12
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wieswe_12c ~                                                           
##     groupfu   (a1)     0.225    0.146    1.539    0.124   -0.062    0.512
##     wiesw_0c  (a2)     0.191    0.076    2.504    0.012    0.042    0.341
##     mskltr_0  (a3)    -0.006    0.003   -1.646    0.100   -0.012    0.001
##     HIE       (a4)    -0.125    0.174   -0.717    0.473   -0.466    0.216
##     LIE       (a5)    -0.154    0.176   -0.871    0.384   -0.499    0.192
##     sex_0     (a6)     0.002    0.148    0.015    0.988   -0.288    0.292
##     age_0c    (a7)    -0.006    0.010   -0.569    0.569   -0.026    0.014
##     bmi_0c    (a8)    -0.027    0.016   -1.727    0.084   -0.058    0.004
##     panvs_0c  (a9)     0.011    0.039    0.295    0.768   -0.064    0.087
##     poshe_0c (a10)     0.067    0.088    0.766    0.443   -0.105    0.240
##     swsrNA_0 (a11)    -0.196    0.074   -2.664    0.008   -0.341   -0.052
##     fam3_0   (a12)     0.134    0.187    0.718    0.473   -0.233    0.502
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     4.846    3.587    1.351    0.177   -2.183   11.876
##     wiesw_0c  (c2)     4.255    1.922    2.214    0.027    0.488    8.022
##     mskltr_0  (c3)     0.447    0.075    5.966    0.000    0.300    0.594
##     HIE       (c4)    -4.690    4.317   -1.086    0.277  -13.152    3.771
##     LIE       (c5)    -0.143    4.391   -0.033    0.974   -8.750    8.463
##     sex_0     (c6)    -2.147    3.645   -0.589    0.556   -9.291    4.997
##     age_0c    (c7)     1.024    0.254    4.036    0.000    0.527    1.521
##     bmi_0c    (c8)    -0.056    0.394   -0.143    0.886   -0.828    0.715
##     panvs_0c  (c9)     2.390    0.951    2.513    0.012    0.526    4.253
##     poshe_0c (c10)     2.028    2.130    0.952    0.341   -2.146    6.201
##     swsrNA_0 (c11)    -2.272    1.773   -1.282    0.200   -5.748    1.203
##     fam3_0   (c12)    -2.659    4.755   -0.559    0.576  -11.977    6.660
##     wisw_12c  (b1)    -0.760    2.041   -0.372    0.710   -4.759    3.240
## 
## Covariances:
##                      Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                              
##     wieswe_0c          -0.063    0.033   -1.932    0.053   -0.127    0.001
##     muskeltrant_0c     -1.338    0.943   -1.418    0.156   -3.186    0.511
##     HIE                 0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE                -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0               0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c             -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c             -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c         -0.154    0.066   -2.343    0.019   -0.282   -0.025
##     poshee_0c           0.004    0.028    0.147    0.883   -0.051    0.059
##     swesourceNA_0c      0.066    0.038    1.718    0.086   -0.009    0.141
##     fam3_0             -0.033    0.013   -2.605    0.009   -0.057   -0.008
##   wieswe_0c ~~                                                            
##     muskeltrant_0c      2.828    1.895    1.493    0.135   -0.885    6.542
##     HIE                 0.020    0.031    0.661    0.509   -0.040    0.080
##     LIE                 0.016    0.031    0.528    0.598   -0.044    0.077
##     sex_0              -0.006    0.031   -0.189    0.850   -0.067    0.056
##     age_0c             -0.958    0.495   -1.934    0.053   -1.928    0.013
##     bmi_0c             -0.816    0.319   -2.554    0.011   -1.442   -0.190
##     painvas_0c         -0.056    0.131   -0.427    0.669   -0.312    0.201
##     poshee_0c           0.272    0.059    4.594    0.000    0.156    0.388
##     swesourceNA_0c     -0.220    0.077   -2.850    0.004   -0.371   -0.069
##     fam3_0              0.020    0.025    0.824    0.410   -0.028    0.069
##   muskeltraint_0c ~~                                                      
##     HIE                 0.537    0.888    0.605    0.545   -1.204    2.278
##     LIE                -0.258    0.890   -0.289    0.772   -2.003    1.487
##     sex_0              -0.916    0.911   -1.005    0.315   -2.701    0.869
##     age_0c             11.641   14.295    0.814    0.415  -16.377   39.659
##     bmi_0c            -24.200    9.267   -2.612    0.009  -42.362   -6.038
##     painvas_0c         -1.195    3.766   -0.317    0.751   -8.577    6.187
##     poshee_0c           2.151    1.642    1.310    0.190   -1.067    5.369
##     swesourceNA_0c     -2.669    2.215   -1.205    0.228   -7.010    1.672
##     fam3_0              1.073    0.724    1.481    0.139   -0.347    2.492
##   HIE ~~                                                                  
##     LIE                -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0              -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c              0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c              0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c         -0.024    0.061   -0.392    0.695   -0.144    0.096
##     poshee_0c           0.045    0.027    1.698    0.090   -0.007    0.098
##     swesourceNA_0c     -0.035    0.036   -0.978    0.328   -0.107    0.036
##     fam3_0              0.002    0.012    0.178    0.858   -0.021    0.025
##   LIE ~~                                                                  
##     sex_0               0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c             -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c             -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c          0.036    0.062    0.588    0.557   -0.085    0.157
##     poshee_0c          -0.040    0.027   -1.513    0.130   -0.093    0.012
##     swesourceNA_0c      0.009    0.036    0.254    0.800   -0.062    0.080
##     fam3_0             -0.006    0.012   -0.544    0.586   -0.029    0.017
##   sex_0 ~~                                                                
##     age_0c              0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c             -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c         -0.007    0.063   -0.106    0.916   -0.130    0.116
##     poshee_0c          -0.034    0.027   -1.263    0.207   -0.088    0.019
##     swesourceNA_0c     -0.073    0.037   -1.961    0.050   -0.146   -0.000
##     fam3_0             -0.025    0.012   -2.099    0.036   -0.049   -0.002
##   age_0c ~~                                                               
##     bmi_0c             -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c         -2.779    0.997   -2.788    0.005   -4.733   -0.825
##     poshee_0c          -1.298    0.434   -2.987    0.003   -2.149   -0.446
##     swesourceNA_0c     -1.100    0.580   -1.895    0.058   -2.237    0.038
##     fam3_0             -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                               
##     painvas_0c          1.817    0.643    2.828    0.005    0.558    3.077
##     poshee_0c           0.085    0.274    0.308    0.758   -0.453    0.622
##     swesourceNA_0c      0.628    0.374    1.680    0.093   -0.105    1.361
##     fam3_0              0.056    0.121    0.462    0.644   -0.181    0.292
##   painvas_0c ~~                                                           
##     poshee_0c          -0.022    0.121   -0.180    0.857   -0.258    0.215
##     swesourceNA_0c      0.358    0.153    2.336    0.019    0.058    0.659
##     fam3_0              0.061    0.051    1.203    0.229   -0.038    0.161
##   poshee_0c ~~                                                            
##     swesourceNA_0c     -0.127    0.067   -1.908    0.056   -0.258    0.003
##     fam3_0              0.002    0.022    0.112    0.911   -0.040    0.045
##   swesourceNA_0c ~~                                                       
##     fam3_0              0.007    0.029    0.222    0.824   -0.051    0.064
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_12c       -0.053    0.152   -0.351    0.726   -0.351    0.245
##    .muskeltrant_24   23.281    3.851    6.045    0.000   15.732   30.829
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     wieswe_0c         0.003    0.065    0.045    0.964   -0.124    0.130
##     muskeltrant_0c    0.000    1.879    0.000    1.000   -3.682    3.682
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c           -0.000    0.489   -0.000    1.000   -0.959    0.959
##     bmi_0c            0.000    0.313    0.000    1.000   -0.614    0.614
##     painvas_0c       -0.012    0.130   -0.096    0.924   -0.267    0.242
##     poshee_0c         0.003    0.056    0.045    0.964   -0.108    0.113
##     swesourceNA_0c   -0.006    0.076   -0.073    0.942   -0.155    0.144
##     fam3_0            0.179    0.025    7.232    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_12c        0.797    0.089    8.980    0.000    0.623    0.970
##    .muskeltrant_24  506.609   54.834    9.239    0.000  399.136  614.083
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     wieswe_0c         1.007    0.092   10.942    0.000    0.827    1.188
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.971    0.366   10.848    0.000    3.254    4.689
##     poshee_0c         0.758    0.069   10.942    0.000    0.622    0.894
##     swesourceNA_0c    1.348    0.125   10.757    0.000    1.103    1.594
##     fam3_0            0.147    0.013   10.957    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.171    0.468   -0.365    0.715   -1.089    0.747
##     total             4.675    3.569    1.310    0.190   -2.320   11.670

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                 0.2254  0.1470 20000  -0.0616    0.5124
## a2                                 0.1912  0.0770 20000   0.0391    0.3420
## a3                                -0.0056  0.0034 20000  -0.0122    0.0011
## a4                                -0.1249  0.1753 20000  -0.4697    0.2206
## a5                                -0.1535  0.1773 20000  -0.5022    0.1937
## a6                                 0.0022  0.1491 20000  -0.2909    0.2928
## a7                                -0.0058  0.0103 20000  -0.0259    0.0146
## a8                                -0.0271  0.0156 20000  -0.0575    0.0037
## a9                                 0.0114  0.0387 20000  -0.0649    0.0881
## a10                                0.0674  0.0889 20000  -0.1085    0.2406
## a11                               -0.1965  0.0733 20000  -0.3421   -0.0542
## a12                                0.1345  0.1876 20000  -0.2387    0.4997
## c1                                 4.8461  3.5703 20000  -2.1464   11.8221
## c2                                 4.2550  1.9143 20000   0.5541    7.9917
## c3                                 0.4472  0.0752 20000   0.2999    0.5968
## c4                                -4.6905  4.3191 20000 -13.1041    3.8012
## c5                                -0.1434  4.3760 20000  -8.9160    8.5150
## c6                                -2.1467  3.5883 20000  -9.2828    4.7990
## c7                                 1.0241  0.2547 20000   0.5289    1.5193
## c8                                -0.0564  0.3913 20000  -0.8312    0.7043
## c9                                 2.3895  0.9522 20000   0.5291    4.2633
## c10                                2.0275  2.1533 20000  -2.2313    6.2432
## c11                               -2.2725  1.7765 20000  -5.6983    1.2384
## c12                               -2.6585  4.7218 20000 -11.8843    6.6029
## b1                                -0.7595  2.0482 20000  -4.7082    3.3146
## groupfu~~wieswe_0c                -0.0630  0.0326 20000  -0.1271    0.0003
## groupfu~~muskeltraint_0c          -1.3377  0.9442 20000  -3.2024    0.4818
## groupfu~~HIE                       0.0069  0.0152 20000  -0.0235    0.0364
## groupfu~~LIE                      -0.0035  0.0152 20000  -0.0331    0.0264
## groupfu~~sex_0                     0.0003  0.0156 20000  -0.0299    0.0312
## groupfu~~age_0c                   -0.0706  0.2453 20000  -0.5433    0.4100
## groupfu~~bmi_0c                   -0.0398  0.1562 20000  -0.3428    0.2672
## groupfu~~painvas_0c               -0.1536  0.0660 20000  -0.2832   -0.0252
## groupfu~~poshee_0c                 0.0041  0.0283 20000  -0.0517    0.0585
## groupfu~~swesourceNA_0c            0.0660  0.0386 20000  -0.0097    0.1412
## groupfu~~fam3_0                   -0.0327  0.0126 20000  -0.0573   -0.0079
## wieswe_0c~~muskeltraint_0c         2.8285  1.8972 20000  -0.8354    6.5873
## wieswe_0c~~HIE                     0.0202  0.0305 20000  -0.0399    0.0794
## wieswe_0c~~LIE                     0.0162  0.0306 20000  -0.0439    0.0764
## wieswe_0c~~sex_0                  -0.0059  0.0316 20000  -0.0682    0.0554
## wieswe_0c~~age_0c                 -0.9575  0.4919 20000  -1.9246   -0.0007
## wieswe_0c~~bmi_0c                 -0.8156  0.3177 20000  -1.4449   -0.1971
## wieswe_0c~~painvas_0c             -0.0559  0.1304 20000  -0.3088    0.1986
## wieswe_0c~~poshee_0c               0.2719  0.0592 20000   0.1567    0.3874
## wieswe_0c~~swesourceNA_0c         -0.2199  0.0777 20000  -0.3724   -0.0673
## wieswe_0c~~fam3_0                  0.0205  0.0250 20000  -0.0288    0.0699
## muskeltraint_0c~~HIE               0.5372  0.8895 20000  -1.2094    2.2920
## muskeltraint_0c~~LIE              -0.2575  0.8865 20000  -1.9867    1.4686
## muskeltraint_0c~~sex_0            -0.9157  0.9110 20000  -2.7003    0.8663
## muskeltraint_0c~~age_0c           11.6412 14.2967 20000 -16.6378   39.6157
## muskeltraint_0c~~bmi_0c          -24.2002  9.2619 20000 -42.4457   -6.2076
## muskeltraint_0c~~painvas_0c       -1.1952  3.7952 20000  -8.7594    6.2543
## muskeltraint_0c~~poshee_0c         2.1512  1.6543 20000  -1.0649    5.3929
## muskeltraint_0c~~swesourceNA_0c   -2.6688  2.2244 20000  -7.0597    1.7180
## muskeltraint_0c~~fam3_0            1.0725  0.7272 20000  -0.3511    2.4875
## HIE~~LIE                          -0.1144  0.0160 20000  -0.1451   -0.0826
## HIE~~sex_0                        -0.0010  0.0148 20000  -0.0300    0.0281
## HIE~~age_0c                        0.1878  0.2322 20000  -0.2680    0.6358
## HIE~~bmi_0c                        0.3432  0.1494 20000   0.0457    0.6326
## HIE~~painvas_0c                   -0.0239  0.0606 20000  -0.1428    0.0956
## HIE~~poshee_0c                     0.0454  0.0266 20000  -0.0070    0.0975
## HIE~~swesourceNA_0c               -0.0355  0.0363 20000  -0.1067    0.0363
## HIE~~fam3_0                        0.0021  0.0117 20000  -0.0213    0.0252
## LIE~~sex_0                         0.0016  0.0148 20000  -0.0275    0.0309
## LIE~~age_0c                       -0.2255  0.2334 20000  -0.6781    0.2327
## LIE~~bmi_0c                       -0.3705  0.1509 20000  -0.6639   -0.0752
## LIE~~painvas_0c                    0.0363  0.0615 20000  -0.0857    0.1563
## LIE~~poshee_0c                    -0.0404  0.0268 20000  -0.0932    0.0117
## LIE~~swesourceNA_0c                0.0092  0.0360 20000  -0.0620    0.0790
## LIE~~fam3_0                       -0.0064  0.0118 20000  -0.0294    0.0168
## sex_0~~age_0c                      0.0533  0.2357 20000  -0.4063    0.5154
## sex_0~~bmi_0c                     -0.1011  0.1525 20000  -0.4032    0.1943
## sex_0~~painvas_0c                 -0.0066  0.0627 20000  -0.1298    0.1154
## sex_0~~poshee_0c                  -0.0344  0.0274 20000  -0.0882    0.0192
## sex_0~~swesourceNA_0c             -0.0728  0.0370 20000  -0.1448   -0.0002
## sex_0~~fam3_0                     -0.0253  0.0121 20000  -0.0492   -0.0013
## age_0c~~bmi_0c                    -1.6174  2.3941 20000  -6.3088    3.0877
## age_0c~~painvas_0c                -2.7791  1.0009 20000  -4.7377   -0.8269
## age_0c~~poshee_0c                 -1.2976  0.4366 20000  -2.1434   -0.4360
## age_0c~~swesourceNA_0c            -1.0997  0.5814 20000  -2.2269    0.0527
## age_0c~~fam3_0                    -0.0283  0.1881 20000  -0.3961    0.3390
## bmi_0c~~painvas_0c                 1.8174  0.6458 20000   0.5468    3.0811
## bmi_0c~~poshee_0c                  0.0846  0.2729 20000  -0.4507    0.6144
## bmi_0c~~swesourceNA_0c             0.6282  0.3736 20000  -0.1063    1.3671
## bmi_0c~~fam3_0                     0.0557  0.1206 20000  -0.1832    0.2911
## painvas_0c~~poshee_0c             -0.0217  0.1202 20000  -0.2595    0.2136
## painvas_0c~~swesourceNA_0c         0.3583  0.1546 20000   0.0559    0.6628
## painvas_0c~~fam3_0                 0.0611  0.0511 20000  -0.0382    0.1614
## poshee_0c~~swesourceNA_0c         -0.1273  0.0666 20000  -0.2565    0.0039
## poshee_0c~~fam3_0                  0.0024  0.0215 20000  -0.0399    0.0439
## swesourceNA_0c~~fam3_0             0.0065  0.0292 20000  -0.0514    0.0636
## wieswe_12c~~wieswe_12c             0.7966  0.0886 20000   0.6246    0.9716
## muskeltraint_24~~muskeltraint_24 506.6094 54.9379 20000 400.3837  615.2510
## groupfu~~groupfu                   0.2499  0.0227 20000   0.2056    0.2945
## wieswe_0c~~wieswe_0c               1.0074  0.0925 20000   0.8271    1.1904
## muskeltraint_0c~~muskeltraint_0c 850.7022 77.2492 20000 698.4997 1000.0850
## HIE~~HIE                           0.2231  0.0204 20000   0.1830    0.2625
## LIE~~LIE                           0.2245  0.0203 20000   0.1844    0.2638
## sex_0~~sex_0                       0.2340  0.0212 20000   0.1931    0.2754
## age_0c~~age_0c                    57.7334  5.2347 20000  47.4847   68.0444
## bmi_0c~~bmi_0c                    23.6379  2.1622 20000  19.4009   27.9038
## painvas_0c~~painvas_0c             3.9714  0.3649 20000   3.2587    4.6808
## poshee_0c~~poshee_0c               0.7583  0.0697 20000   0.6227    0.8944
## swesourceNA_0c~~swesourceNA_0c     1.3482  0.1254 20000   1.1007    1.5931
## fam3_0~~fam3_0                     0.1470  0.0134 20000   0.1210    0.1733
## wieswe_12c~1                      -0.0534  0.1533 20000  -0.3559    0.2433
## muskeltraint_24~1                 23.2806  3.8371 20000  15.7750   30.9650
## groupfu~1                          0.5104  0.0321 20000   0.4477    0.5737
## wieswe_0c~1                        0.0029  0.0653 20000  -0.1257    0.1315
## muskeltraint_0c~1                  0.0000  1.8677 20000  -3.6045    3.6657
## HIE~1                              0.3361  0.0304 20000   0.2765    0.3956
## LIE~1                              0.3402  0.0306 20000   0.2805    0.4004
## sex_0~1                            0.3734  0.0312 20000   0.3125    0.4340
## age_0c~1                           0.0000  0.4907 20000  -0.9642    0.9698
## bmi_0c~1                           0.0000  0.3135 20000  -0.6194    0.6056
## painvas_0c~1                      -0.0124  0.1296 20000  -0.2652    0.2407
## poshee_0c~1                        0.0025  0.0562 20000  -0.1072    0.1127
## swesourceNA_0c~1                  -0.0056  0.0764 20000  -0.1549    0.1460
## fam3_0~1                           0.1790  0.0247 20000   0.1310    0.2281
## ind                               -0.1712  0.5564 20000  -1.4060    0.9574
## total                              4.6750  3.5644 20000  -2.2707   11.6101

Recovery Self-Efficacy at 18 Months

model <- '
# Direct Effects
wieswe_18c ~ a1*groupfu + a2*wieswe_0c + a3*muskeltraint_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
muskeltraint_24 ~ c1*groupfu + c2*wieswe_0c + c3*muskeltraint_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*wieswe_18c

# Covariances
groupfu ~~ wieswe_0c + muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
wieswe_0c ~~ muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
muskeltraint_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 393 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        13
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   wieswe_18c ~                                                           
##     groupfu   (a1)    -0.073    0.164   -0.442    0.658   -0.395    0.249
##     wiesw_0c  (a2)     0.188    0.089    2.110    0.035    0.013    0.362
##     mskltr_0  (a3)     0.003    0.004    0.819    0.413   -0.005    0.012
##     HIE       (a4)     0.295    0.198    1.493    0.135   -0.092    0.683
##     LIE       (a5)     0.030    0.200    0.148    0.882   -0.362    0.421
##     sex_0     (a6)     0.007    0.167    0.042    0.966   -0.321    0.335
##     age_0c    (a7)    -0.006    0.012   -0.479    0.632   -0.029    0.018
##     bmi_0c    (a8)    -0.026    0.018   -1.470    0.142   -0.061    0.009
##     panvs_0c  (a9)     0.046    0.045    1.029    0.303   -0.042    0.134
##     poshe_0c (a10)     0.166    0.097    1.708    0.088   -0.025    0.356
##     swsrNA_0 (a11)    -0.147    0.086   -1.720    0.085   -0.315    0.021
##     fam3_0   (a12)     0.023    0.209    0.112    0.911   -0.387    0.434
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     4.778    3.572    1.338    0.181   -2.223   11.778
##     wiesw_0c  (c2)     3.952    1.913    2.065    0.039    0.201    7.702
##     mskltr_0  (c3)     0.450    0.074    6.083    0.000    0.305    0.595
##     HIE       (c4)    -4.808    4.339   -1.108    0.268  -13.312    3.697
##     LIE       (c5)     0.056    4.371    0.013    0.990   -8.511    8.623
##     sex_0     (c6)    -2.075    3.644   -0.570    0.569   -9.217    5.066
##     age_0c    (c7)     1.034    0.254    4.074    0.000    0.537    1.532
##     bmi_0c    (c8)    -0.013    0.393   -0.032    0.975   -0.783    0.758
##     panvs_0c  (c9)     2.351    0.954    2.465    0.014    0.482    4.220
##     poshe_0c (c10)     1.867    2.149    0.869    0.385   -2.345    6.079
##     swsrNA_0 (c11)    -1.954    1.745   -1.120    0.263   -5.373    1.465
##     fam3_0   (c12)    -2.740    4.754   -0.576    0.564  -12.057    6.577
##     wisw_18c  (b1)     0.872    1.931    0.452    0.651   -2.912    4.656
## 
## Covariances:
##                      Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                              
##     wieswe_0c          -0.063    0.033   -1.932    0.053   -0.127    0.001
##     muskeltrant_0c     -1.338    0.943   -1.418    0.156   -3.186    0.511
##     HIE                 0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE                -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0               0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c             -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c             -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c         -0.153    0.066   -2.342    0.019   -0.282   -0.025
##     poshee_0c           0.004    0.028    0.150    0.881   -0.051    0.059
##     swesourceNA_0c      0.065    0.038    1.695    0.090   -0.010    0.140
##     fam3_0             -0.033    0.013   -2.605    0.009   -0.057   -0.008
##   wieswe_0c ~~                                                            
##     muskeltrant_0c      2.828    1.895    1.493    0.135   -0.885    6.542
##     HIE                 0.020    0.031    0.661    0.509   -0.040    0.080
##     LIE                 0.016    0.031    0.528    0.597   -0.044    0.077
##     sex_0              -0.006    0.031   -0.189    0.850   -0.067    0.056
##     age_0c             -0.958    0.495   -1.934    0.053   -1.928    0.013
##     bmi_0c             -0.816    0.319   -2.554    0.011   -1.442   -0.190
##     painvas_0c         -0.056    0.131   -0.427    0.670   -0.312    0.201
##     poshee_0c           0.272    0.059    4.596    0.000    0.156    0.388
##     swesourceNA_0c     -0.218    0.077   -2.833    0.005   -0.370   -0.067
##     fam3_0              0.020    0.025    0.824    0.410   -0.028    0.069
##   muskeltraint_0c ~~                                                      
##     HIE                 0.537    0.888    0.605    0.545   -1.204    2.278
##     LIE                -0.258    0.890   -0.289    0.772   -2.003    1.487
##     sex_0              -0.916    0.911   -1.005    0.315   -2.701    0.869
##     age_0c             11.641   14.295    0.814    0.415  -16.377   39.659
##     bmi_0c            -24.200    9.267   -2.612    0.009  -42.362   -6.038
##     painvas_0c         -1.220    3.765   -0.324    0.746   -8.600    6.160
##     poshee_0c           2.151    1.642    1.310    0.190   -1.067    5.369
##     swesourceNA_0c     -2.623    2.214   -1.184    0.236   -6.963    1.718
##     fam3_0              1.073    0.724    1.481    0.139   -0.347    2.492
##   HIE ~~                                                                  
##     LIE                -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0              -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c              0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c              0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c         -0.024    0.061   -0.393    0.694   -0.144    0.096
##     poshee_0c           0.046    0.027    1.702    0.089   -0.007    0.098
##     swesourceNA_0c     -0.037    0.036   -1.011    0.312   -0.108    0.034
##     fam3_0              0.002    0.012    0.178    0.858   -0.021    0.025
##   LIE ~~                                                                  
##     sex_0               0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c             -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c             -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c          0.036    0.062    0.576    0.565   -0.085    0.156
##     poshee_0c          -0.040    0.027   -1.515    0.130   -0.093    0.012
##     swesourceNA_0c      0.011    0.036    0.292    0.770   -0.060    0.081
##     fam3_0             -0.006    0.012   -0.544    0.587   -0.029    0.017
##   sex_0 ~~                                                                
##     age_0c              0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c             -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c         -0.007    0.063   -0.109    0.913   -0.130    0.116
##     poshee_0c          -0.034    0.027   -1.265    0.206   -0.088    0.019
##     swesourceNA_0c     -0.073    0.037   -1.968    0.049   -0.146   -0.000
##     fam3_0             -0.025    0.012   -2.100    0.036   -0.049   -0.002
##   age_0c ~~                                                               
##     bmi_0c             -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c         -2.775    0.997   -2.785    0.005   -4.728   -0.822
##     poshee_0c          -1.297    0.434   -2.986    0.003   -2.149   -0.446
##     swesourceNA_0c     -1.091    0.580   -1.881    0.060   -2.228    0.046
##     fam3_0             -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                               
##     painvas_0c          1.816    0.643    2.826    0.005    0.556    3.075
##     poshee_0c           0.086    0.274    0.314    0.754   -0.451    0.624
##     swesourceNA_0c      0.620    0.374    1.656    0.098   -0.114    1.353
##     fam3_0              0.056    0.121    0.463    0.644   -0.181    0.292
##   painvas_0c ~~                                                           
##     poshee_0c          -0.025    0.121   -0.209    0.835   -0.261    0.211
##     swesourceNA_0c      0.355    0.153    2.315    0.021    0.054    0.655
##     fam3_0              0.060    0.051    1.189    0.234   -0.039    0.160
##   poshee_0c ~~                                                            
##     swesourceNA_0c     -0.127    0.067   -1.898    0.058   -0.257    0.004
##     fam3_0              0.002    0.022    0.110    0.912   -0.040    0.045
##   swesourceNA_0c ~~                                                       
##     fam3_0              0.006    0.029    0.209    0.834   -0.051    0.063
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_18c       -0.046    0.174   -0.262    0.793   -0.386    0.295
##    .muskeltrant_24   23.286    3.851    6.046    0.000   15.738   30.834
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     wieswe_0c         0.003    0.065    0.045    0.964   -0.124    0.130
##     muskeltrant_0c   -0.000    1.879   -0.000    1.000   -3.682    3.682
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c           -0.000    0.489   -0.000    1.000   -0.959    0.959
##     bmi_0c            0.000    0.313    0.000    1.000   -0.614    0.614
##     painvas_0c       -0.012    0.130   -0.095    0.924   -0.266    0.242
##     poshee_0c         0.003    0.056    0.048    0.962   -0.107    0.113
##     swesourceNA_0c   -0.004    0.076   -0.050    0.960   -0.153    0.146
##     fam3_0            0.179    0.025    7.232    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .wieswe_18c        0.940    0.109    8.643    0.000    0.726    1.153
##    .muskeltrant_24  506.537   54.833    9.238    0.000  399.066  614.008
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     wieswe_0c         1.007    0.092   10.942    0.000    0.827    1.188
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.970    0.366   10.853    0.000    3.253    4.686
##     poshee_0c         0.758    0.069   10.941    0.000    0.623    0.894
##     swesourceNA_0c    1.347    0.125   10.765    0.000    1.102    1.592
##     fam3_0            0.147    0.013   10.957    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.063    0.194   -0.327    0.744   -0.443    0.317
##     total             4.714    3.569    1.321    0.186   -2.280   11.708

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%    97.5%
## a1                                -0.0726  0.1641 20000  -0.3959   0.2465
## a2                                 0.1876  0.0883 20000   0.0149   0.3583
## a3                                 0.0034  0.0042 20000  -0.0048   0.0115
## a4                                 0.2951  0.1984 20000  -0.0938   0.6845
## a5                                 0.0296  0.1999 20000  -0.3617   0.4245
## a6                                 0.0071  0.1676 20000  -0.3222   0.3400
## a7                                -0.0057  0.0119 20000  -0.0290   0.0176
## a8                                -0.0261  0.0179 20000  -0.0610   0.0089
## a9                                 0.0463  0.0449 20000  -0.0413   0.1352
## a10                                0.1659  0.0967 20000  -0.0206   0.3557
## a11                               -0.1473  0.0856 20000  -0.3147   0.0205
## a12                                0.0234  0.2101 20000  -0.3920   0.4311
## c1                                 4.7775  3.5590 20000  -2.3181  11.5977
## c2                                 3.9515  1.9351 20000   0.1722   7.7727
## c3                                 0.4499  0.0739 20000   0.3053   0.5964
## c4                                -4.8077  4.3213 20000 -13.3413   3.7780
## c5                                 0.0559  4.3715 20000  -8.5304   8.6458
## c6                                -2.0755  3.6028 20000  -9.0310   4.9895
## c7                                 1.0345  0.2553 20000   0.5325   1.5332
## c8                                -0.0125  0.3898 20000  -0.7829   0.7562
## c9                                 2.3507  0.9574 20000   0.4930   4.2213
## c10                                1.8673  2.1427 20000  -2.3239   6.1192
## c11                               -1.9541  1.7544 20000  -5.3521   1.5799
## c12                               -2.7396  4.6990 20000 -11.9545   6.5058
## b1                                 0.8724  1.9400 20000  -2.9268   4.6480
## groupfu~~wieswe_0c                -0.0630  0.0325 20000  -0.1271   0.0004
## groupfu~~muskeltraint_0c          -1.3377  0.9392 20000  -3.1680   0.5044
## groupfu~~HIE                       0.0069  0.0151 20000  -0.0229   0.0364
## groupfu~~LIE                      -0.0035  0.0151 20000  -0.0331   0.0264
## groupfu~~sex_0                     0.0003  0.0156 20000  -0.0303   0.0303
## groupfu~~age_0c                   -0.0706  0.2448 20000  -0.5549   0.4107
## groupfu~~bmi_0c                   -0.0398  0.1567 20000  -0.3453   0.2675
## groupfu~~painvas_0c               -0.1535  0.0657 20000  -0.2841  -0.0273
## groupfu~~poshee_0c                 0.0042  0.0282 20000  -0.0514   0.0588
## groupfu~~swesourceNA_0c            0.0651  0.0382 20000  -0.0095   0.1404
## groupfu~~fam3_0                   -0.0327  0.0126 20000  -0.0576  -0.0080
## wieswe_0c~~muskeltraint_0c         2.8285  1.8954 20000  -0.9295   6.5193
## wieswe_0c~~HIE                     0.0202  0.0307 20000  -0.0399   0.0803
## wieswe_0c~~LIE                     0.0162  0.0308 20000  -0.0448   0.0763
## wieswe_0c~~sex_0                  -0.0059  0.0315 20000  -0.0672   0.0563
## wieswe_0c~~age_0c                 -0.9575  0.4976 20000  -1.9234   0.0104
## wieswe_0c~~bmi_0c                 -0.8156  0.3182 20000  -1.4405  -0.1891
## wieswe_0c~~painvas_0c             -0.0558  0.1311 20000  -0.3119   0.2027
## wieswe_0c~~poshee_0c               0.2721  0.0587 20000   0.1569   0.3871
## wieswe_0c~~swesourceNA_0c         -0.2185  0.0773 20000  -0.3704  -0.0672
## wieswe_0c~~fam3_0                  0.0205  0.0249 20000  -0.0281   0.0690
## muskeltraint_0c~~HIE               0.5372  0.8925 20000  -1.2333   2.2757
## muskeltraint_0c~~LIE              -0.2575  0.8892 20000  -2.0115   1.4787
## muskeltraint_0c~~sex_0            -0.9157  0.9141 20000  -2.7334   0.8556
## muskeltraint_0c~~age_0c           11.6412 14.2850 20000 -16.4651  39.6985
## muskeltraint_0c~~bmi_0c          -24.2002  9.2817 20000 -42.2915  -5.8069
## muskeltraint_0c~~painvas_0c       -1.2196  3.7960 20000  -8.6190   6.3139
## muskeltraint_0c~~poshee_0c         2.1511  1.6539 20000  -1.0856   5.4097
## muskeltraint_0c~~swesourceNA_0c   -2.6227  2.2265 20000  -6.9884   1.7042
## muskeltraint_0c~~fam3_0            1.0725  0.7258 20000  -0.3448   2.4816
## HIE~~LIE                          -0.1144  0.0163 20000  -0.1464  -0.0821
## HIE~~sex_0                        -0.0010  0.0147 20000  -0.0297   0.0275
## HIE~~age_0c                        0.1878  0.2328 20000  -0.2684   0.6406
## HIE~~bmi_0c                        0.3432  0.1500 20000   0.0476   0.6397
## HIE~~painvas_0c                   -0.0240  0.0612 20000  -0.1433   0.0969
## HIE~~poshee_0c                     0.0455  0.0266 20000  -0.0069   0.0976
## HIE~~swesourceNA_0c               -0.0367  0.0361 20000  -0.1073   0.0344
## HIE~~fam3_0                        0.0021  0.0117 20000  -0.0207   0.0249
## LIE~~sex_0                         0.0016  0.0148 20000  -0.0279   0.0307
## LIE~~age_0c                       -0.2255  0.2347 20000  -0.6880   0.2395
## LIE~~bmi_0c                       -0.3705  0.1504 20000  -0.6638  -0.0727
## LIE~~painvas_0c                    0.0355  0.0618 20000  -0.0859   0.1569
## LIE~~poshee_0c                    -0.0405  0.0266 20000  -0.0923   0.0117
## LIE~~swesourceNA_0c                0.0106  0.0360 20000  -0.0605   0.0808
## LIE~~fam3_0                       -0.0064  0.0118 20000  -0.0293   0.0168
## sex_0~~age_0c                      0.0533  0.2362 20000  -0.4047   0.5186
## sex_0~~bmi_0c                     -0.1011  0.1528 20000  -0.3966   0.1977
## sex_0~~painvas_0c                 -0.0068  0.0628 20000  -0.1317   0.1143
## sex_0~~poshee_0c                  -0.0345  0.0272 20000  -0.0878   0.0187
## sex_0~~swesourceNA_0c             -0.0731  0.0372 20000  -0.1462   0.0004
## sex_0~~fam3_0                     -0.0253  0.0121 20000  -0.0492  -0.0014
## age_0c~~bmi_0c                    -1.6174  2.4098 20000  -6.3928   3.0609
## age_0c~~painvas_0c                -2.7752  1.0007 20000  -4.7301  -0.8228
## age_0c~~poshee_0c                 -1.2972  0.4323 20000  -2.1515  -0.4553
## age_0c~~swesourceNA_0c            -1.0911  0.5814 20000  -2.2249   0.0646
## age_0c~~fam3_0                    -0.0283  0.1884 20000  -0.3967   0.3411
## bmi_0c~~painvas_0c                 1.8156  0.6413 20000   0.5616   3.0854
## bmi_0c~~poshee_0c                  0.0860  0.2748 20000  -0.4560   0.6214
## bmi_0c~~swesourceNA_0c             0.6196  0.3725 20000  -0.1195   1.3440
## bmi_0c~~fam3_0                     0.0558  0.1204 20000  -0.1783   0.2945
## painvas_0c~~poshee_0c             -0.0252  0.1213 20000  -0.2629   0.2145
## painvas_0c~~swesourceNA_0c         0.3548  0.1524 20000   0.0535   0.6490
## painvas_0c~~fam3_0                 0.0604  0.0511 20000  -0.0387   0.1608
## poshee_0c~~swesourceNA_0c         -0.1266  0.0661 20000  -0.2556   0.0019
## poshee_0c~~fam3_0                  0.0024  0.0216 20000  -0.0404   0.0443
## swesourceNA_0c~~fam3_0             0.0061  0.0293 20000  -0.0517   0.0635
## wieswe_18c~~wieswe_18c             0.9396  0.1097 20000   0.7271   1.1550
## muskeltraint_24~~muskeltraint_24 506.5366 54.9366 20000 400.3154 615.1757
## groupfu~~groupfu                   0.2499  0.0227 20000   0.2051   0.2945
## wieswe_0c~~wieswe_0c               1.0074  0.0924 20000   0.8252   1.1870
## muskeltraint_0c~~muskeltraint_0c 850.7024 77.2485 20000 698.3914 999.9928
## HIE~~HIE                           0.2231  0.0203 20000   0.1831   0.2626
## LIE~~LIE                           0.2245  0.0207 20000   0.1837   0.2647
## sex_0~~sex_0                       0.2340  0.0212 20000   0.1927   0.2758
## age_0c~~age_0c                    57.7334  5.2341 20000  47.4141  68.0121
## bmi_0c~~bmi_0c                    23.6379  2.1632 20000  19.4669  27.9118
## painvas_0c~~painvas_0c             3.9696  0.3636 20000   3.2553   4.6747
## poshee_0c~~poshee_0c               0.7584  0.0692 20000   0.6217   0.8943
## swesourceNA_0c~~swesourceNA_0c     1.3469  0.1265 20000   1.0997   1.5951
## fam3_0~~fam3_0                     0.1470  0.0135 20000   0.1211   0.1734
## wieswe_18c~1                      -0.0456  0.1752 20000  -0.3905   0.2967
## muskeltraint_24~1                 23.2857  3.8458 20000  15.7097  30.8674
## groupfu~1                          0.5104  0.0319 20000   0.4476   0.5727
## wieswe_0c~1                        0.0029  0.0647 20000  -0.1231   0.1293
## muskeltraint_0c~1                  0.0000  1.8677 20000  -3.6027   3.6704
## HIE~1                              0.3361  0.0304 20000   0.2762   0.3954
## LIE~1                              0.3402  0.0304 20000   0.2803   0.3991
## sex_0~1                            0.3734  0.0312 20000   0.3123   0.4351
## age_0c~1                           0.0000  0.4908 20000  -0.9638   0.9686
## bmi_0c~1                           0.0000  0.3135 20000  -0.6092   0.6181
## painvas_0c~1                      -0.0123  0.1299 20000  -0.2661   0.2437
## poshee_0c~1                        0.0027  0.0565 20000  -0.1069   0.1136
## swesourceNA_0c~1                  -0.0038  0.0768 20000  -0.1560   0.1462
## fam3_0~1                           0.1790  0.0248 20000   0.1300   0.2272
## ind                               -0.0634  0.3723 20000  -0.9108   0.7095
## total                              4.7142  3.5714 20000  -2.3955  11.5750

Action Control at 12 Months

model <- '
# Direct Effects
hk_12c ~ a1*groupfu + a2*hk_0c + a3*muskeltraint_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
muskeltraint_24 ~ c1*groupfu + c2*hk_0c + c3*muskeltraint_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*hk_12c

# Covariances
groupfu ~~ hk_0c + muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
hk_0c ~~ muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
muskeltraint_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 412 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        11
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   hk_12c ~                                                               
##     groupfu   (a1)     0.315    0.178    1.773    0.076   -0.033    0.664
##     hk_0c     (a2)     0.345    0.065    5.314    0.000    0.218    0.472
##     mskltr_0  (a3)    -0.000    0.004   -0.060    0.952   -0.008    0.008
##     HIE       (a4)     0.201    0.214    0.940    0.347   -0.218    0.620
##     LIE       (a5)     0.403    0.216    1.862    0.063   -0.021    0.827
##     sex_0     (a6)     0.117    0.182    0.642    0.521   -0.239    0.473
##     age_0c    (a7)     0.036    0.013    2.766    0.006    0.010    0.061
##     bmi_0c    (a8)     0.010    0.019    0.553    0.580   -0.027    0.048
##     panvs_0c  (a9)     0.037    0.047    0.776    0.438   -0.056    0.130
##     poshe_0c (a10)     0.043    0.109    0.391    0.696   -0.171    0.256
##     swsrNA_0 (a11)    -0.149    0.093   -1.609    0.108   -0.332    0.033
##     fam3_0   (a12)    -0.007    0.228   -0.029    0.977   -0.454    0.441
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     4.459    3.613    1.234    0.217   -2.624   11.541
##     hk_0c     (c2)     2.503    1.411    1.774    0.076   -0.262    5.269
##     mskltr_0  (c3)     0.453    0.074    6.104    0.000    0.307    0.598
##     HIE       (c4)    -4.845    4.353   -1.113    0.266  -13.377    3.687
##     LIE       (c5)     0.541    4.433    0.122    0.903   -8.148    9.230
##     sex_0     (c6)    -2.395    3.672   -0.652    0.514   -9.591    4.802
##     age_0c    (c7)     0.889    0.268    3.312    0.001    0.363    1.415
##     bmi_0c    (c8)    -0.171    0.384   -0.445    0.656   -0.924    0.582
##     panvs_0c  (c9)     2.253    0.955    2.360    0.018    0.382    4.125
##     poshe_0c (c10)     2.688    2.083    1.290    0.197   -1.395    6.771
##     swsrNA_0 (c11)    -2.715    1.717   -1.581    0.114   -6.080    0.650
##     fam3_0   (c12)    -2.244    4.778   -0.470    0.639  -11.608    7.119
##     hk_12c    (b1)    -0.677    1.682   -0.403    0.687   -3.975    2.620
## 
## Covariances:
##                      Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                              
##     hk_0c              -0.040    0.046   -0.874    0.382   -0.130    0.050
##     muskeltrant_0c     -1.338    0.943   -1.418    0.156   -3.186    0.511
##     HIE                 0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE                -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0               0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c             -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c             -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c         -0.154    0.066   -2.354    0.019   -0.283   -0.026
##     poshee_0c           0.004    0.028    0.126    0.900   -0.052    0.059
##     swesourceNA_0c      0.070    0.039    1.812    0.070   -0.006    0.145
##     fam3_0             -0.033    0.013   -2.607    0.009   -0.057   -0.008
##   hk_0c ~~                                                                
##     muskeltrant_0c      7.040    2.705    2.602    0.009    1.738   12.342
##     HIE                 0.047    0.043    1.092    0.275   -0.038    0.132
##     LIE                -0.001    0.043   -0.013    0.989   -0.085    0.084
##     sex_0              -0.022    0.044   -0.490    0.624   -0.108    0.065
##     age_0c              2.159    0.709    3.047    0.002    0.770    3.548
##     bmi_0c             -0.668    0.447   -1.496    0.135   -1.544    0.207
##     painvas_0c         -0.067    0.184   -0.363    0.717   -0.428    0.294
##     poshee_0c           0.169    0.081    2.086    0.037    0.010    0.327
##     swesourceNA_0c     -0.134    0.109   -1.225    0.221   -0.348    0.080
##     fam3_0              0.003    0.035    0.074    0.941   -0.066    0.071
##   muskeltraint_0c ~~                                                      
##     HIE                 0.537    0.888    0.605    0.545   -1.204    2.278
##     LIE                -0.258    0.890   -0.289    0.772   -2.003    1.487
##     sex_0              -0.916    0.911   -1.005    0.315   -2.701    0.869
##     age_0c             11.641   14.295    0.814    0.415  -16.377   39.659
##     bmi_0c            -24.200    9.267   -2.612    0.009  -42.362   -6.038
##     painvas_0c         -1.248    3.767   -0.331    0.740   -8.630    6.135
##     poshee_0c           2.152    1.641    1.312    0.190   -1.064    5.368
##     swesourceNA_0c     -2.627    2.218   -1.184    0.236   -6.974    1.721
##     fam3_0              1.073    0.724    1.482    0.138   -0.346    2.493
##   HIE ~~                                                                  
##     LIE                -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0              -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c              0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c              0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c         -0.023    0.061   -0.379    0.705   -0.143    0.097
##     poshee_0c           0.045    0.027    1.668    0.095   -0.008    0.097
##     swesourceNA_0c     -0.034    0.036   -0.938    0.348   -0.105    0.037
##     fam3_0              0.002    0.012    0.180    0.857   -0.021    0.025
##   LIE ~~                                                                  
##     sex_0               0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c             -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c             -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c          0.034    0.062    0.558    0.577   -0.087    0.155
##     poshee_0c          -0.040    0.027   -1.498    0.134   -0.092    0.012
##     swesourceNA_0c      0.009    0.036    0.255    0.798   -0.062    0.080
##     fam3_0             -0.006    0.012   -0.546    0.585   -0.029    0.017
##   sex_0 ~~                                                                
##     age_0c              0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c             -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c         -0.007    0.063   -0.104    0.917   -0.129    0.116
##     poshee_0c          -0.034    0.027   -1.247    0.212   -0.087    0.019
##     swesourceNA_0c     -0.077    0.037   -2.063    0.039   -0.150   -0.004
##     fam3_0             -0.025    0.012   -2.098    0.036   -0.049   -0.002
##   age_0c ~~                                                               
##     bmi_0c             -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c         -2.767    0.997   -2.776    0.005   -4.721   -0.814
##     poshee_0c          -1.301    0.434   -2.995    0.003   -2.151   -0.450
##     swesourceNA_0c     -1.077    0.581   -1.853    0.064   -2.216    0.062
##     fam3_0             -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                               
##     painvas_0c          1.828    0.643    2.843    0.004    0.568    3.088
##     poshee_0c           0.074    0.274    0.271    0.787   -0.463    0.612
##     swesourceNA_0c      0.668    0.375    1.781    0.075   -0.067    1.404
##     fam3_0              0.055    0.121    0.460    0.645   -0.181    0.292
##   painvas_0c ~~                                                           
##     poshee_0c          -0.027    0.120   -0.225    0.822   -0.263    0.209
##     swesourceNA_0c      0.350    0.154    2.283    0.022    0.050    0.651
##     fam3_0              0.061    0.051    1.200    0.230   -0.039    0.161
##   poshee_0c ~~                                                            
##     swesourceNA_0c     -0.123    0.067   -1.846    0.065   -0.254    0.008
##     fam3_0              0.003    0.022    0.123    0.902   -0.040    0.045
##   swesourceNA_0c ~~                                                       
##     fam3_0              0.006    0.029    0.207    0.836   -0.051    0.064
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_12c           -0.442    0.188   -2.353    0.019   -0.810   -0.074
##    .muskeltrant_24   23.125    3.928    5.887    0.000   15.425   30.824
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     hk_0c            -0.000    0.091   -0.000    1.000   -0.179    0.179
##     muskeltrant_0c   -0.000    1.879   -0.000    1.000   -3.682    3.682
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c           -0.000    0.489   -0.000    1.000   -0.959    0.959
##     bmi_0c            0.000    0.313    0.000    1.000   -0.614    0.614
##     painvas_0c       -0.012    0.130   -0.093    0.926   -0.266    0.242
##     poshee_0c         0.001    0.056    0.023    0.982   -0.109    0.111
##     swesourceNA_0c    0.001    0.076    0.018    0.986   -0.149    0.151
##     fam3_0            0.179    0.025    7.230    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_12c            1.185    0.132    8.948    0.000    0.926    1.445
##    .muskeltrant_24  512.245   55.435    9.240    0.000  403.594  620.895
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     hk_0c             2.015    0.184   10.977    0.000    1.655    2.375
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.972    0.366   10.847    0.000    3.254    4.690
##     poshee_0c         0.757    0.069   10.954    0.000    0.622    0.893
##     swesourceNA_0c    1.351    0.126   10.733    0.000    1.104    1.598
##     fam3_0            0.147    0.013   10.956    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.214    0.539   -0.396    0.692   -1.271    0.844
##     total             4.245    3.578    1.186    0.235   -2.768   11.258

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                 0.3154  0.1789 20000  -0.0400    0.6637
## a2                                 0.3450  0.0651 20000   0.2165    0.4706
## a3                                -0.0002  0.0041 20000  -0.0083    0.0078
## a4                                 0.2008  0.2150 20000  -0.2241    0.6213
## a5                                 0.4029  0.2175 20000  -0.0288    0.8257
## a6                                 0.1167  0.1813 20000  -0.2385    0.4732
## a7                                 0.0358  0.0130 20000   0.0104    0.0612
## a8                                 0.0105  0.0189 20000  -0.0269    0.0475
## a9                                 0.0368  0.0473 20000  -0.0560    0.1290
## a10                                0.0425  0.1086 20000  -0.1712    0.2545
## a11                               -0.1495  0.0930 20000  -0.3336    0.0321
## a12                               -0.0066  0.2303 20000  -0.4538    0.4428
## c1                                 4.4586  3.5677 20000  -2.4335   11.5694
## c2                                 2.5032  1.4141 20000  -0.2937    5.2597
## c3                                 0.4526  0.0746 20000   0.3061    0.5998
## c4                                -4.8447  4.3377 20000 -13.3508    3.8296
## c5                                 0.5411  4.4419 20000  -8.1740    9.2400
## c6                                -2.3948  3.6483 20000  -9.6009    4.6935
## c7                                 0.8889  0.2685 20000   0.3636    1.4218
## c8                                -0.1710  0.3809 20000  -0.9194    0.5743
## c9                                 2.2534  0.9572 20000   0.3879    4.1151
## c10                                2.6882  2.0917 20000  -1.4413    6.8115
## c11                               -2.7152  1.7258 20000  -6.0875    0.6555
## c12                               -2.2444  4.7291 20000 -11.5381    7.0069
## b1                                -0.6773  1.6837 20000  -3.9751    2.6579
## groupfu~~hk_0c                    -0.0400  0.0459 20000  -0.1301    0.0504
## groupfu~~muskeltraint_0c          -1.3377  0.9426 20000  -3.1918    0.4813
## groupfu~~HIE                       0.0069  0.0153 20000  -0.0228    0.0368
## groupfu~~LIE                      -0.0035  0.0152 20000  -0.0330    0.0263
## groupfu~~sex_0                     0.0003  0.0155 20000  -0.0298    0.0311
## groupfu~~age_0c                   -0.0706  0.2449 20000  -0.5508    0.4066
## groupfu~~bmi_0c                   -0.0398  0.1568 20000  -0.3505    0.2679
## groupfu~~painvas_0c               -0.1543  0.0660 20000  -0.2834   -0.0234
## groupfu~~poshee_0c                 0.0035  0.0279 20000  -0.0523    0.0578
## groupfu~~swesourceNA_0c            0.0698  0.0386 20000  -0.0067    0.1454
## groupfu~~fam3_0                   -0.0327  0.0125 20000  -0.0571   -0.0084
## hk_0c~~muskeltraint_0c             7.0401  2.7275 20000   1.7164   12.3696
## hk_0c~~HIE                         0.0473  0.0430 20000  -0.0372    0.1316
## hk_0c~~LIE                        -0.0006  0.0432 20000  -0.0845    0.0852
## hk_0c~~sex_0                      -0.0217  0.0443 20000  -0.1078    0.0650
## hk_0c~~age_0c                      2.1591  0.7063 20000   0.7775    3.5299
## hk_0c~~bmi_0c                     -0.6683  0.4451 20000  -1.5622    0.1892
## hk_0c~~painvas_0c                 -0.0669  0.1840 20000  -0.4263    0.2962
## hk_0c~~poshee_0c                   0.1687  0.0807 20000   0.0094    0.3275
## hk_0c~~swesourceNA_0c             -0.1337  0.1088 20000  -0.3479    0.0785
## hk_0c~~fam3_0                      0.0026  0.0353 20000  -0.0664    0.0710
## muskeltraint_0c~~HIE               0.5372  0.8853 20000  -1.1950    2.2990
## muskeltraint_0c~~LIE              -0.2575  0.8953 20000  -1.9891    1.4950
## muskeltraint_0c~~sex_0            -0.9157  0.9132 20000  -2.7148    0.8721
## muskeltraint_0c~~age_0c           11.6412 14.2956 20000 -16.6511   39.5860
## muskeltraint_0c~~bmi_0c          -24.2002  9.2596 20000 -42.4352   -6.2287
## muskeltraint_0c~~painvas_0c       -1.2477  3.7968 20000  -8.7815    6.2300
## muskeltraint_0c~~poshee_0c         2.1522  1.6489 20000  -1.1101    5.3547
## muskeltraint_0c~~swesourceNA_0c   -2.6266  2.2258 20000  -6.9362    1.7447
## muskeltraint_0c~~fam3_0            1.0733  0.7281 20000  -0.3486    2.4887
## HIE~~LIE                          -0.1144  0.0161 20000  -0.1460   -0.0830
## HIE~~sex_0                        -0.0010  0.0147 20000  -0.0299    0.0274
## HIE~~age_0c                        0.1878  0.2335 20000  -0.2685    0.6434
## HIE~~bmi_0c                        0.3432  0.1490 20000   0.0529    0.6356
## HIE~~painvas_0c                   -0.0231  0.0613 20000  -0.1447    0.0951
## HIE~~poshee_0c                     0.0446  0.0268 20000  -0.0080    0.0971
## HIE~~swesourceNA_0c               -0.0341  0.0364 20000  -0.1060    0.0373
## HIE~~fam3_0                        0.0021  0.0117 20000  -0.0208    0.0253
## LIE~~sex_0                         0.0016  0.0148 20000  -0.0275    0.0305
## LIE~~age_0c                       -0.2255  0.2335 20000  -0.6808    0.2334
## LIE~~bmi_0c                       -0.3705  0.1519 20000  -0.6684   -0.0700
## LIE~~painvas_0c                    0.0344  0.0615 20000  -0.0852    0.1554
## LIE~~poshee_0c                    -0.0400  0.0267 20000  -0.0930    0.0121
## LIE~~swesourceNA_0c                0.0092  0.0363 20000  -0.0611    0.0814
## LIE~~fam3_0                       -0.0064  0.0118 20000  -0.0298    0.0166
## sex_0~~age_0c                      0.0533  0.2360 20000  -0.4087    0.5139
## sex_0~~bmi_0c                     -0.1011  0.1514 20000  -0.3972    0.1981
## sex_0~~painvas_0c                 -0.0065  0.0626 20000  -0.1289    0.1150
## sex_0~~poshee_0c                  -0.0340  0.0271 20000  -0.0874    0.0196
## sex_0~~swesourceNA_0c             -0.0768  0.0373 20000  -0.1495   -0.0030
## sex_0~~fam3_0                     -0.0253  0.0122 20000  -0.0491   -0.0015
## age_0c~~bmi_0c                    -1.6174  2.3904 20000  -6.3021    3.0408
## age_0c~~painvas_0c                -2.7672  1.0001 20000  -4.7462   -0.8313
## age_0c~~poshee_0c                 -1.3005  0.4352 20000  -2.1603   -0.4563
## age_0c~~swesourceNA_0c            -1.0766  0.5838 20000  -2.2251    0.0713
## age_0c~~fam3_0                    -0.0283  0.1878 20000  -0.3963    0.3426
## bmi_0c~~painvas_0c                 1.8277  0.6471 20000   0.5613    3.0930
## bmi_0c~~poshee_0c                  0.0742  0.2745 20000  -0.4726    0.6133
## bmi_0c~~swesourceNA_0c             0.6682  0.3736 20000  -0.0648    1.3922
## bmi_0c~~fam3_0                     0.0555  0.1198 20000  -0.1805    0.2891
## painvas_0c~~poshee_0c             -0.0271  0.1216 20000  -0.2666    0.2095
## painvas_0c~~swesourceNA_0c         0.3504  0.1546 20000   0.0464    0.6549
## painvas_0c~~fam3_0                 0.0610  0.0510 20000  -0.0359    0.1615
## poshee_0c~~swesourceNA_0c         -0.1232  0.0670 20000  -0.2557    0.0077
## poshee_0c~~fam3_0                  0.0026  0.0212 20000  -0.0390    0.0446
## swesourceNA_0c~~fam3_0             0.0061  0.0293 20000  -0.0511    0.0631
## hk_12c~~hk_12c                     1.1852  0.1320 20000   0.9267    1.4434
## muskeltraint_24~~muskeltraint_24 512.2446 55.5396 20000 404.8573  622.0770
## groupfu~~groupfu                   0.2499  0.0229 20000   0.2058    0.2954
## hk_0c~~hk_0c                       2.0150  0.1842 20000   1.6506    2.3750
## muskeltraint_0c~~muskeltraint_0c 850.7023 77.2492 20000 698.5125 1000.1061
## HIE~~HIE                           0.2231  0.0201 20000   0.1839    0.2630
## LIE~~LIE                           0.2245  0.0203 20000   0.1851    0.2644
## sex_0~~sex_0                       0.2340  0.0213 20000   0.1927    0.2754
## age_0c~~age_0c                    57.7334  5.2341 20000  47.5095   68.0353
## bmi_0c~~bmi_0c                    23.6379  2.1668 20000  19.3397   27.7992
## painvas_0c~~painvas_0c             3.9720  0.3648 20000   3.2644    4.6953
## poshee_0c~~poshee_0c               0.7573  0.0689 20000   0.6204    0.8921
## swesourceNA_0c~~swesourceNA_0c     1.3510  0.1265 20000   1.1027    1.5986
## fam3_0~~fam3_0                     0.1470  0.0134 20000   0.1207    0.1730
## hk_12c~1                          -0.4417  0.1880 20000  -0.8061   -0.0715
## muskeltraint_24~1                 23.1246  3.9233 20000  15.2692   30.7374
## groupfu~1                          0.5104  0.0323 20000   0.4473    0.5742
## hk_0c~1                            0.0000  0.0918 20000  -0.1808    0.1799
## muskeltraint_0c~1                  0.0000  1.8677 20000  -3.6742    3.6038
## HIE~1                              0.3361  0.0301 20000   0.2770    0.3951
## LIE~1                              0.3402  0.0305 20000   0.2802    0.4002
## sex_0~1                            0.3734  0.0311 20000   0.3123    0.4346
## age_0c~1                           0.0000  0.4879 20000  -0.9522    0.9649
## bmi_0c~1                           0.0000  0.3123 20000  -0.6093    0.6210
## painvas_0c~1                      -0.0121  0.1300 20000  -0.2691    0.2405
## poshee_0c~1                        0.0013  0.0560 20000  -0.1080    0.1096
## swesourceNA_0c~1                   0.0014  0.0769 20000  -0.1481    0.1538
## fam3_0~1                           0.1789  0.0247 20000   0.1307    0.2283
## ind                               -0.2136  0.6107 20000  -1.5787    1.0227
## total                              4.2449  3.5473 20000  -2.6137   11.3002

Action Control at 18 Months

model <- '
# Direct Effects
hk_18c ~ a1*groupfu + a2*hk_0c + a3*muskeltraint_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
muskeltraint_24 ~ c1*groupfu + c2*hk_0c + c3*muskeltraint_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*hk_18c

# Covariances
groupfu ~~ hk_0c + muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
hk_0c ~~ muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
muskeltraint_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 413 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        12
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   hk_18c ~                                                               
##     groupfu   (a1)     0.267    0.168    1.587    0.113   -0.063    0.597
##     hk_0c     (a2)     0.319    0.062    5.190    0.000    0.199    0.440
##     mskltr_0  (a3)     0.001    0.004    0.284    0.776   -0.007    0.009
##     HIE       (a4)     0.369    0.204    1.811    0.070   -0.030    0.769
##     LIE       (a5)     0.226    0.206    1.096    0.273   -0.178    0.630
##     sex_0     (a6)    -0.175    0.172   -1.016    0.310   -0.513    0.163
##     age_0c    (a7)     0.014    0.012    1.119    0.263   -0.010    0.038
##     bmi_0c    (a8)     0.016    0.018    0.881    0.378   -0.019    0.051
##     panvs_0c  (a9)     0.084    0.047    1.805    0.071   -0.007    0.176
##     poshe_0c (a10)     0.178    0.098    1.823    0.068   -0.013    0.369
##     swsrNA_0 (a11)    -0.145    0.085   -1.717    0.086   -0.311    0.021
##     fam3_0   (a12)     0.036    0.217    0.165    0.869   -0.389    0.460
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     4.433    3.621    1.224    0.221   -2.664   11.530
##     hk_0c     (c2)     2.486    1.427    1.742    0.081   -0.311    5.283
##     mskltr_0  (c3)     0.453    0.074    6.113    0.000    0.308    0.599
##     HIE       (c4)    -4.739    4.397   -1.078    0.281  -13.358    3.880
##     LIE       (c5)     0.468    4.412    0.106    0.915   -8.180    9.116
##     sex_0     (c6)    -2.506    3.680   -0.681    0.496   -9.718    4.706
##     age_0c    (c7)     0.879    0.264    3.325    0.001    0.361    1.396
##     bmi_0c    (c8)    -0.173    0.384   -0.449    0.653   -0.926    0.580
##     panvs_0c  (c9)     2.298    0.967    2.376    0.017    0.403    4.194
##     poshe_0c (c10)     2.814    2.114    1.331    0.183   -1.331    6.958
##     swsrNA_0 (c11)    -2.783    1.731   -1.608    0.108   -6.175    0.610
##     fam3_0   (c12)    -2.207    4.778   -0.462    0.644  -11.573    7.158
##     hk_18c    (b1)    -0.750    1.935   -0.388    0.698   -4.542    3.042
## 
## Covariances:
##                      Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                              
##     hk_0c              -0.040    0.046   -0.874    0.382   -0.130    0.050
##     muskeltrant_0c     -1.338    0.943   -1.418    0.156   -3.186    0.511
##     HIE                 0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE                -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0               0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c             -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c             -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c         -0.153    0.065   -2.342    0.019   -0.282   -0.025
##     poshee_0c           0.004    0.028    0.147    0.883   -0.051    0.059
##     swesourceNA_0c      0.068    0.038    1.778    0.075   -0.007    0.144
##     fam3_0             -0.033    0.013   -2.607    0.009   -0.057   -0.008
##   hk_0c ~~                                                                
##     muskeltrant_0c      7.040    2.705    2.602    0.009    1.738   12.342
##     HIE                 0.047    0.043    1.092    0.275   -0.038    0.132
##     LIE                -0.001    0.043   -0.013    0.989   -0.085    0.084
##     sex_0              -0.022    0.044   -0.490    0.624   -0.108    0.065
##     age_0c              2.159    0.709    3.047    0.002    0.770    3.548
##     bmi_0c             -0.668    0.447   -1.496    0.135   -1.544    0.207
##     painvas_0c         -0.067    0.184   -0.363    0.716   -0.428    0.294
##     poshee_0c           0.172    0.081    2.126    0.033    0.013    0.331
##     swesourceNA_0c     -0.143    0.109   -1.314    0.189   -0.357    0.071
##     fam3_0              0.003    0.035    0.074    0.941   -0.066    0.071
##   muskeltraint_0c ~~                                                      
##     HIE                 0.537    0.888    0.605    0.545   -1.204    2.278
##     LIE                -0.258    0.890   -0.289    0.772   -2.003    1.487
##     sex_0              -0.916    0.911   -1.005    0.315   -2.701    0.869
##     age_0c             11.641   14.295    0.814    0.415  -16.377   39.659
##     bmi_0c            -24.200    9.267   -2.612    0.009  -42.362   -6.038
##     painvas_0c         -1.202    3.763   -0.319    0.749   -8.578    6.174
##     poshee_0c           2.151    1.642    1.310    0.190   -1.067    5.369
##     swesourceNA_0c     -2.620    2.216   -1.182    0.237   -6.964    1.723
##     fam3_0              1.073    0.724    1.482    0.138   -0.346    2.493
##   HIE ~~                                                                  
##     LIE                -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0              -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c              0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c              0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c         -0.024    0.061   -0.392    0.695   -0.144    0.096
##     poshee_0c           0.045    0.027    1.698    0.090   -0.007    0.098
##     swesourceNA_0c     -0.036    0.036   -0.985    0.325   -0.107    0.035
##     fam3_0              0.002    0.012    0.180    0.858   -0.021    0.025
##   LIE ~~                                                                  
##     sex_0               0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c             -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c             -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c          0.034    0.062    0.556    0.578   -0.087    0.155
##     poshee_0c          -0.040    0.027   -1.513    0.130   -0.093    0.012
##     swesourceNA_0c      0.010    0.036    0.266    0.790   -0.061    0.080
##     fam3_0             -0.006    0.012   -0.546    0.585   -0.029    0.017
##   sex_0 ~~                                                                
##     age_0c              0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c             -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c         -0.007    0.063   -0.114    0.910   -0.130    0.116
##     poshee_0c          -0.034    0.027   -1.263    0.207   -0.088    0.019
##     swesourceNA_0c     -0.075    0.037   -2.014    0.044   -0.148   -0.002
##     fam3_0             -0.025    0.012   -2.098    0.036   -0.049   -0.002
##   age_0c ~~                                                               
##     bmi_0c             -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c         -2.759    0.996   -2.771    0.006   -4.711   -0.808
##     poshee_0c          -1.298    0.434   -2.987    0.003   -2.149   -0.446
##     swesourceNA_0c     -1.091    0.581   -1.879    0.060   -2.229    0.047
##     fam3_0             -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                               
##     painvas_0c          1.818    0.642    2.832    0.005    0.560    3.077
##     poshee_0c           0.085    0.274    0.309    0.758   -0.453    0.622
##     swesourceNA_0c      0.637    0.374    1.701    0.089   -0.097    1.371
##     fam3_0              0.055    0.121    0.460    0.645   -0.181    0.292
##   painvas_0c ~~                                                           
##     poshee_0c          -0.030    0.120   -0.252    0.801   -0.266    0.205
##     swesourceNA_0c      0.350    0.153    2.283    0.022    0.050    0.650
##     fam3_0              0.060    0.051    1.190    0.234   -0.039    0.160
##   poshee_0c ~~                                                            
##     swesourceNA_0c     -0.124    0.067   -1.865    0.062   -0.255    0.006
##     fam3_0              0.002    0.022    0.112    0.911   -0.040    0.045
##   swesourceNA_0c ~~                                                       
##     fam3_0              0.007    0.029    0.228    0.820   -0.051    0.064
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_18c           -0.292    0.178   -1.644    0.100   -0.641    0.056
##    .muskeltrant_24   23.151    3.914    5.915    0.000   15.480   30.822
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     hk_0c            -0.000    0.091   -0.000    1.000   -0.179    0.179
##     muskeltrant_0c   -0.000    1.879   -0.000    1.000   -3.682    3.682
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c           -0.000    0.489   -0.000    1.000   -0.959    0.959
##     bmi_0c            0.000    0.313    0.000    1.000   -0.614    0.614
##     painvas_0c       -0.011    0.130   -0.084    0.933   -0.265    0.243
##     poshee_0c         0.003    0.056    0.045    0.964   -0.108    0.113
##     swesourceNA_0c   -0.002    0.076   -0.023    0.981   -0.151    0.148
##     fam3_0            0.179    0.025    7.230    0.000    0.130    0.227
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .hk_18c            0.997    0.115    8.680    0.000    0.772    1.222
##    .muskeltrant_24  512.141   55.429    9.240    0.000  403.501  620.780
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     hk_0c             2.015    0.184   10.977    0.000    1.655    2.375
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.966    0.365   10.860    0.000    3.251    4.682
##     poshee_0c         0.758    0.069   10.940    0.000    0.622    0.894
##     swesourceNA_0c    1.348    0.125   10.753    0.000    1.103    1.594
##     fam3_0            0.147    0.013   10.956    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind              -0.200    0.524   -0.382    0.702   -1.228    0.827
##     total             4.233    3.577    1.183    0.237   -2.777   11.243

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                 0.2673  0.1681 20000  -0.0636    0.6002
## a2                                 0.3192  0.0613 20000   0.1983    0.4396
## a3                                 0.0011  0.0040 20000  -0.0066    0.0089
## a4                                 0.3691  0.2047 20000  -0.0305    0.7737
## a5                                 0.2261  0.2078 20000  -0.1814    0.6379
## a6                                -0.1750  0.1738 20000  -0.5180    0.1666
## a7                                 0.0139  0.0125 20000  -0.0108    0.0380
## a8                                 0.0158  0.0178 20000  -0.0193    0.0505
## a9                                 0.0845  0.0466 20000  -0.0062    0.1765
## a10                                0.1779  0.0976 20000  -0.0147    0.3689
## a11                               -0.1452  0.0851 20000  -0.3124    0.0213
## a12                                0.0357  0.2155 20000  -0.3842    0.4572
## c1                                 4.4330  3.5988 20000  -2.7100   11.4546
## c2                                 2.4863  1.4332 20000  -0.2949    5.3125
## c3                                 0.4533  0.0748 20000   0.3071    0.6014
## c4                                -4.7391  4.3828 20000 -13.2439    3.8857
## c5                                 0.4683  4.4029 20000  -8.0768    9.3246
## c6                                -2.5060  3.6907 20000  -9.6459    4.8286
## c7                                 0.8785  0.2667 20000   0.3523    1.3975
## c8                                -0.1727  0.3805 20000  -0.9083    0.5806
## c9                                 2.2985  0.9697 20000   0.3847    4.1930
## c10                                2.8137  2.1221 20000  -1.3502    7.0009
## c11                               -2.7826  1.7284 20000  -6.2136    0.5594
## c12                               -2.2075  4.7480 20000 -11.3599    7.1501
## b1                                -0.7497  1.9353 20000  -4.5357    3.0668
## groupfu~~hk_0c                    -0.0400  0.0459 20000  -0.1301    0.0505
## groupfu~~muskeltraint_0c          -1.3377  0.9496 20000  -3.2019    0.5225
## groupfu~~HIE                       0.0069  0.0153 20000  -0.0234    0.0367
## groupfu~~LIE                      -0.0035  0.0154 20000  -0.0334    0.0267
## groupfu~~sex_0                     0.0003  0.0157 20000  -0.0303    0.0311
## groupfu~~age_0c                   -0.0706  0.2452 20000  -0.5514    0.4089
## groupfu~~bmi_0c                   -0.0398  0.1564 20000  -0.3426    0.2711
## groupfu~~painvas_0c               -0.1534  0.0657 20000  -0.2825   -0.0228
## groupfu~~poshee_0c                 0.0041  0.0281 20000  -0.0511    0.0594
## groupfu~~swesourceNA_0c            0.0684  0.0386 20000  -0.0078    0.1435
## groupfu~~fam3_0                   -0.0327  0.0124 20000  -0.0574   -0.0086
## hk_0c~~muskeltraint_0c             7.0401  2.7176 20000   1.6887   12.3438
## hk_0c~~HIE                         0.0473  0.0432 20000  -0.0359    0.1333
## hk_0c~~LIE                        -0.0006  0.0430 20000  -0.0845    0.0841
## hk_0c~~sex_0                      -0.0217  0.0442 20000  -0.1085    0.0646
## hk_0c~~age_0c                      2.1591  0.7118 20000   0.7569    3.5584
## hk_0c~~bmi_0c                     -0.6683  0.4473 20000  -1.5274    0.2189
## hk_0c~~painvas_0c                 -0.0669  0.1832 20000  -0.4240    0.2944
## hk_0c~~poshee_0c                   0.1721  0.0809 20000   0.0140    0.3302
## hk_0c~~swesourceNA_0c             -0.1433  0.1098 20000  -0.3598    0.0712
## hk_0c~~fam3_0                      0.0026  0.0349 20000  -0.0653    0.0701
## muskeltraint_0c~~HIE               0.5372  0.8871 20000  -1.2046    2.2786
## muskeltraint_0c~~LIE              -0.2575  0.8909 20000  -2.0179    1.4714
## muskeltraint_0c~~sex_0            -0.9157  0.9090 20000  -2.6898    0.8365
## muskeltraint_0c~~age_0c           11.6412 14.2838 20000 -16.5897   39.6766
## muskeltraint_0c~~bmi_0c          -24.2002  9.2820 20000 -42.4596   -6.1366
## muskeltraint_0c~~painvas_0c       -1.2020  3.7384 20000  -8.4477    6.1622
## muskeltraint_0c~~poshee_0c         2.1512  1.6531 20000  -1.1345    5.3679
## muskeltraint_0c~~swesourceNA_0c   -2.6204  2.2333 20000  -7.0228    1.6816
## muskeltraint_0c~~fam3_0            1.0733  0.7243 20000  -0.3265    2.5151
## HIE~~LIE                          -0.1144  0.0161 20000  -0.1460   -0.0827
## HIE~~sex_0                        -0.0010  0.0148 20000  -0.0297    0.0283
## HIE~~age_0c                        0.1878  0.2325 20000  -0.2680    0.6446
## HIE~~bmi_0c                        0.3432  0.1502 20000   0.0488    0.6351
## HIE~~painvas_0c                   -0.0239  0.0613 20000  -0.1441    0.0944
## HIE~~poshee_0c                     0.0454  0.0268 20000  -0.0073    0.0983
## HIE~~swesourceNA_0c               -0.0357  0.0366 20000  -0.1081    0.0358
## HIE~~fam3_0                        0.0021  0.0117 20000  -0.0207    0.0253
## LIE~~sex_0                         0.0016  0.0148 20000  -0.0271    0.0304
## LIE~~age_0c                       -0.2255  0.2337 20000  -0.6866    0.2296
## LIE~~bmi_0c                       -0.3705  0.1511 20000  -0.6648   -0.0713
## LIE~~painvas_0c                    0.0343  0.0618 20000  -0.0859    0.1559
## LIE~~poshee_0c                    -0.0404  0.0267 20000  -0.0922    0.0120
## LIE~~swesourceNA_0c                0.0096  0.0360 20000  -0.0602    0.0799
## LIE~~fam3_0                       -0.0064  0.0117 20000  -0.0296    0.0163
## sex_0~~age_0c                      0.0533  0.2360 20000  -0.4085    0.5160
## sex_0~~bmi_0c                     -0.1011  0.1518 20000  -0.3962    0.1944
## sex_0~~painvas_0c                 -0.0071  0.0625 20000  -0.1307    0.1155
## sex_0~~poshee_0c                  -0.0344  0.0272 20000  -0.0875    0.0188
## sex_0~~swesourceNA_0c             -0.0749  0.0373 20000  -0.1478   -0.0014
## sex_0~~fam3_0                     -0.0253  0.0121 20000  -0.0489   -0.0015
## age_0c~~bmi_0c                    -1.6174  2.4011 20000  -6.3309    3.1058
## age_0c~~painvas_0c                -2.7595  0.9942 20000  -4.6951   -0.8208
## age_0c~~poshee_0c                 -1.2975  0.4356 20000  -2.1521   -0.4547
## age_0c~~swesourceNA_0c            -1.0910  0.5789 20000  -2.2262    0.0591
## age_0c~~fam3_0                    -0.0283  0.1889 20000  -0.3994    0.3423
## bmi_0c~~painvas_0c                 1.8184  0.6425 20000   0.5460    3.0882
## bmi_0c~~poshee_0c                  0.0847  0.2708 20000  -0.4547    0.6169
## bmi_0c~~swesourceNA_0c             0.6369  0.3750 20000  -0.0977    1.3745
## bmi_0c~~fam3_0                     0.0555  0.1196 20000  -0.1775    0.2906
## painvas_0c~~poshee_0c             -0.0303  0.1200 20000  -0.2637    0.2071
## painvas_0c~~swesourceNA_0c         0.3499  0.1531 20000   0.0448    0.6503
## painvas_0c~~fam3_0                 0.0604  0.0509 20000  -0.0412    0.1590
## poshee_0c~~swesourceNA_0c         -0.1245  0.0673 20000  -0.2556    0.0068
## poshee_0c~~fam3_0                  0.0024  0.0214 20000  -0.0391    0.0448
## swesourceNA_0c~~fam3_0             0.0067  0.0292 20000  -0.0515    0.0640
## hk_18c~~hk_18c                     0.9966  0.1159 20000   0.7679    1.2203
## muskeltraint_24~~muskeltraint_24 512.1408 55.5339 20000 402.3237  619.5173
## groupfu~~groupfu                   0.2499  0.0230 20000   0.2040    0.2943
## hk_0c~~hk_0c                       2.0150  0.1831 20000   1.6550    2.3707
## muskeltraint_0c~~muskeltraint_0c 850.7023 77.2485 20000 701.4021 1002.9982
## HIE~~HIE                           0.2231  0.0202 20000   0.1835    0.2634
## LIE~~LIE                           0.2245  0.0205 20000   0.1845    0.2652
## sex_0~~sex_0                       0.2340  0.0212 20000   0.1920    0.2752
## age_0c~~age_0c                    57.7334  5.2352 20000  47.4847   68.0219
## bmi_0c~~bmi_0c                    23.6379  2.1579 20000  19.4186   27.8204
## painvas_0c~~painvas_0c             3.9664  0.3659 20000   3.2426    4.6897
## poshee_0c~~poshee_0c               0.7584  0.0694 20000   0.6229    0.8945
## swesourceNA_0c~~swesourceNA_0c     1.3485  0.1251 20000   1.1037    1.5959
## fam3_0~~fam3_0                     0.1470  0.0134 20000   0.1208    0.1735
## hk_18c~1                          -0.2922  0.1790 20000  -0.6391    0.0611
## muskeltraint_24~1                 23.1510  3.9041 20000  15.4237   30.8208
## groupfu~1                          0.5104  0.0323 20000   0.4469    0.5730
## hk_0c~1                            0.0000  0.0909 20000  -0.1769    0.1773
## muskeltraint_0c~1                  0.0000  1.8880 20000  -3.7491    3.6640
## HIE~1                              0.3361  0.0304 20000   0.2765    0.3960
## LIE~1                              0.3402  0.0302 20000   0.2813    0.3998
## sex_0~1                            0.3734  0.0311 20000   0.3129    0.4342
## age_0c~1                           0.0000  0.4883 20000  -0.9633    0.9487
## bmi_0c~1                           0.0000  0.3118 20000  -0.6106    0.6133
## painvas_0c~1                      -0.0109  0.1298 20000  -0.2672    0.2411
## poshee_0c~1                        0.0025  0.0568 20000  -0.1072    0.1139
## swesourceNA_0c~1                  -0.0018  0.0766 20000  -0.1528    0.1493
## fam3_0~1                           0.1789  0.0247 20000   0.1311    0.2276
## ind                               -0.2004  0.6149 20000  -1.5677    1.0382
## total                              4.2326  3.5680 20000  -2.8174   11.2208

Collaborative Planning at 18 Months

model <- '
# Direct Effects
collimpint_18c ~ a1*groupfu + a2*collimpint_0c + a3*muskeltraint_0c + a4*HIE + a5*LIE + a6*sex_0 + a7*age_0c + a8*bmi_0c + a9*painvas_0c + a10*poshee_0c + a11*swesourceNA_0c + a12*fam3_0
muskeltraint_24 ~ c1*groupfu + c2*collimpint_0c + c3*muskeltraint_0c + c4*HIE + c5*LIE + c6*sex_0 + c7*age_0c + c8*bmi_0c + c9*painvas_0c + c10*poshee_0c + c11*swesourceNA_0c + c12*fam3_0 + b1*collimpint_18c

# Covariances
groupfu ~~ collimpint_0c + muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
collimpint_0c ~~ muskeltraint_0c + HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
muskeltraint_0c ~~ HIE + LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
HIE ~~ LIE + sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
LIE ~~ sex_0 + age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
sex_0 ~~ age_0c + bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
age_0c ~~ bmi_0c + painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
bmi_0c ~~ painvas_0c + poshee_0c + swesourceNA_0c + fam3_0
painvas_0c ~~ poshee_0c + swesourceNA_0c + fam3_0
poshee_0c ~~ swesourceNA_0c + fam3_0
swesourceNA_0c ~~ fam3_0

# Indirect Effect
ind := a1*b1

# Total Effect
total := ind + c1
'
model_fit <- sem(data = data, model = model, missing = "FIML")
summary(model_fit, ci = TRUE)

## lavaan 0.6-18 ended normally after 451 iterations
## 
##   Estimator                                         ML
##   Optimization method                           NLMINB
##   Number of model parameters                       119
## 
##   Number of observations                           241
##   Number of missing patterns                        18
## 
## Model Test User Model:
##                                                       
##   Test statistic                                 0.000
##   Degrees of freedom                                 0
## 
## Parameter Estimates:
## 
##   Standard errors                             Standard
##   Information                                 Observed
##   Observed information based on                Hessian
## 
## Regressions:
##                     Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   collimpint_18c ~                                                       
##     groupfu   (a1)     0.084    0.384    0.219    0.827   -0.669    0.837
##     cllmpn_0  (a2)     0.476    0.090    5.310    0.000    0.301    0.652
##     mskltr_0  (a3)    -0.026    0.009   -2.782    0.005   -0.045   -0.008
##     HIE       (a4)    -0.269    0.422   -0.638    0.523   -1.097    0.558
##     LIE       (a5)    -0.575    0.451   -1.273    0.203   -1.459    0.310
##     sex_0     (a6)    -0.117    0.375   -0.313    0.754   -0.851    0.617
##     age_0c    (a7)     0.066    0.029    2.283    0.022    0.009    0.124
##     bmi_0c    (a8)    -0.060    0.036   -1.647    0.100   -0.131    0.011
##     panvs_0c  (a9)    -0.057    0.106   -0.537    0.591   -0.265    0.151
##     poshe_0c (a10)     0.455    0.221    2.059    0.040    0.022    0.888
##     swsrNA_0 (a11)    -0.071    0.201   -0.353    0.724   -0.465    0.323
##     fam3_0   (a12)     0.002    0.530    0.003    0.998   -1.038    1.041
##   muskeltraint_24 ~                                                      
##     groupfu   (c1)     3.353    3.630    0.924    0.356   -3.762   10.468
##     cllmpn_0  (c2)     0.686    1.375    0.499    0.618   -2.009    3.382
##     mskltr_0  (c3)     0.489    0.086    5.667    0.000    0.320    0.658
##     HIE       (c4)    -4.200    4.384   -0.958    0.338  -12.792    4.393
##     LIE       (c5)     1.370    4.511    0.304    0.761   -7.472   10.211
##     sex_0     (c6)    -2.625    3.713   -0.707    0.480   -9.902    4.652
##     age_0c    (c7)     0.832    0.280    2.971    0.003    0.283    1.381
##     bmi_0c    (c8)    -0.070    0.397   -0.177    0.860   -0.849    0.709
##     panvs_0c  (c9)     2.280    0.965    2.363    0.018    0.389    4.172
##     poshe_0c (c10)     2.204    2.225    0.991    0.322   -2.157    6.565
##     swsrNA_0 (c11)    -2.351    1.741   -1.350    0.177   -5.764    1.062
##     fam3_0   (c12)    -1.919    4.825   -0.398    0.691  -11.375    7.538
##     cllmp_18  (b1)     1.751    1.570    1.115    0.265   -1.326    4.827
## 
## Covariances:
##                      Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##   groupfu ~~                                                              
##     collimpint_0c      -0.007    0.092   -0.081    0.936   -0.188    0.173
##     muskeltrant_0c     -1.338    0.943   -1.418    0.156   -3.186    0.511
##     HIE                 0.007    0.015    0.453    0.651   -0.023    0.037
##     LIE                -0.004    0.015   -0.231    0.817   -0.033    0.026
##     sex_0               0.000    0.016    0.018    0.986   -0.030    0.031
##     age_0c             -0.071    0.245   -0.288    0.773   -0.550    0.409
##     bmi_0c             -0.040    0.157   -0.254    0.799   -0.347    0.267
##     painvas_0c         -0.154    0.066   -2.346    0.019   -0.282   -0.025
##     poshee_0c           0.003    0.028    0.097    0.922   -0.052    0.058
##     swesourceNA_0c      0.070    0.038    1.810    0.070   -0.006    0.145
##     fam3_0             -0.033    0.013   -2.603    0.009   -0.057   -0.008
##   collimpint_0c ~~                                                        
##     muskeltrant_0c      5.766    5.301    1.088    0.277   -4.623   16.155
##     HIE                 0.064    0.083    0.764    0.445   -0.100    0.227
##     LIE                 0.017    0.090    0.183    0.855   -0.160    0.193
##     sex_0               0.043    0.091    0.471    0.638   -0.135    0.220
##     age_0c              0.252    1.422    0.177    0.859   -2.535    3.039
##     bmi_0c             -0.636    0.889   -0.716    0.474   -2.379    1.106
##     painvas_0c         -0.008    0.383   -0.022    0.983   -0.758    0.742
##     poshee_0c           0.245    0.161    1.524    0.127   -0.070    0.559
##     swesourceNA_0c     -0.321    0.235   -1.363    0.173   -0.782    0.140
##     fam3_0             -0.074    0.083   -0.894    0.371   -0.236    0.088
##   muskeltraint_0c ~~                                                      
##     HIE                 0.537    0.888    0.605    0.545   -1.204    2.278
##     LIE                -0.258    0.890   -0.289    0.772   -2.003    1.487
##     sex_0              -0.916    0.911   -1.005    0.315   -2.701    0.869
##     age_0c             11.641   14.295    0.814    0.415  -16.377   39.659
##     bmi_0c            -24.200    9.267   -2.612    0.009  -42.362   -6.038
##     painvas_0c         -1.214    3.766   -0.322    0.747   -8.595    6.167
##     poshee_0c           2.154    1.640    1.313    0.189   -1.061    5.369
##     swesourceNA_0c     -2.610    2.218   -1.177    0.239   -6.957    1.736
##     fam3_0              1.071    0.724    1.479    0.139   -0.348    2.491
##   HIE ~~                                                                  
##     LIE                -0.114    0.016   -7.064    0.000   -0.146   -0.083
##     sex_0              -0.001    0.015   -0.070    0.944   -0.030    0.028
##     age_0c              0.188    0.232    0.811    0.417   -0.266    0.642
##     bmi_0c              0.343    0.150    2.294    0.022    0.050    0.636
##     painvas_0c         -0.023    0.061   -0.384    0.701   -0.143    0.096
##     poshee_0c           0.044    0.027    1.629    0.103   -0.009    0.096
##     swesourceNA_0c     -0.036    0.036   -0.982    0.326   -0.107    0.036
##     fam3_0              0.002    0.012    0.176    0.860   -0.021    0.025
##   LIE ~~                                                                  
##     sex_0               0.002    0.015    0.106    0.915   -0.027    0.031
##     age_0c             -0.225    0.232   -0.970    0.332   -0.681    0.230
##     bmi_0c             -0.371    0.150   -2.465    0.014   -0.665   -0.076
##     painvas_0c          0.036    0.062    0.576    0.565   -0.085    0.156
##     poshee_0c          -0.039    0.027   -1.478    0.139   -0.092    0.013
##     swesourceNA_0c      0.009    0.036    0.253    0.800   -0.062    0.080
##     fam3_0             -0.006    0.012   -0.540    0.589   -0.029    0.017
##   sex_0 ~~                                                                
##     age_0c              0.053    0.237    0.225    0.822   -0.411    0.517
##     bmi_0c             -0.101    0.152   -0.667    0.505   -0.398    0.196
##     painvas_0c         -0.007    0.063   -0.112    0.911   -0.130    0.116
##     poshee_0c          -0.033    0.027   -1.225    0.220   -0.087    0.020
##     swesourceNA_0c     -0.075    0.037   -2.024    0.043   -0.148   -0.002
##     fam3_0             -0.025    0.012   -2.102    0.036   -0.049   -0.002
##   age_0c ~~                                                               
##     bmi_0c             -1.617    2.382   -0.679    0.497   -6.286    3.051
##     painvas_0c         -2.761    0.997   -2.770    0.006   -4.714   -0.807
##     poshee_0c          -1.304    0.434   -3.005    0.003   -2.155   -0.454
##     swesourceNA_0c     -1.097    0.581   -1.889    0.059   -2.236    0.042
##     fam3_0             -0.028    0.188   -0.151    0.880   -0.396    0.340
##   bmi_0c ~~                                                               
##     painvas_0c          1.825    0.643    2.840    0.005    0.565    3.085
##     poshee_0c           0.061    0.274    0.221    0.825   -0.476    0.598
##     swesourceNA_0c      0.652    0.375    1.739    0.082   -0.083    1.386
##     fam3_0              0.056    0.121    0.466    0.641   -0.180    0.292
##   painvas_0c ~~                                                           
##     poshee_0c          -0.025    0.120   -0.206    0.837   -0.261    0.211
##     swesourceNA_0c      0.354    0.154    2.304    0.021    0.053    0.655
##     fam3_0              0.062    0.051    1.228    0.219   -0.037    0.162
##   poshee_0c ~~                                                            
##     swesourceNA_0c     -0.123    0.067   -1.849    0.064   -0.254    0.007
##     fam3_0              0.003    0.022    0.134    0.893   -0.039    0.045
##   swesourceNA_0c ~~                                                       
##     fam3_0              0.005    0.029    0.187    0.852   -0.052    0.063
## 
## Intercepts:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_18c    0.168    0.368    0.456    0.649   -0.554    0.889
##    .muskeltrant_24   23.691    3.942    6.010    0.000   15.964   31.417
##     groupfu           0.510    0.032   15.850    0.000    0.447    0.573
##     collimpint_0c    -0.159    0.186   -0.856    0.392   -0.524    0.205
##     muskeltrant_0c    0.000    1.879    0.000    1.000   -3.682    3.682
##     HIE               0.336    0.030   11.046    0.000    0.276    0.396
##     LIE               0.340    0.031   11.149    0.000    0.280    0.400
##     sex_0             0.373    0.031   11.985    0.000    0.312    0.435
##     age_0c            0.000    0.489    0.000    1.000   -0.959    0.959
##     bmi_0c           -0.000    0.313   -0.000    1.000   -0.614    0.614
##     painvas_0c       -0.011    0.130   -0.081    0.935   -0.265    0.244
##     poshee_0c        -0.000    0.056   -0.006    0.995   -0.110    0.110
##     swesourceNA_0c   -0.001    0.076   -0.016    0.987   -0.151    0.149
##     fam3_0            0.179    0.025    7.235    0.000    0.131    0.228
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##    .collimpint_18c    2.270    0.386    5.882    0.000    1.514    3.026
##    .muskeltrant_24  506.241   55.775    9.076    0.000  396.924  615.559
##     groupfu           0.250    0.023   10.977    0.000    0.205    0.295
##     collimpint_0c     4.431    0.560    7.914    0.000    3.334    5.529
##     muskeltrant_0c  850.702   77.497   10.977    0.000  698.811 1002.593
##     HIE               0.223    0.020   10.977    0.000    0.183    0.263
##     LIE               0.224    0.020   10.977    0.000    0.184    0.265
##     sex_0             0.234    0.021   10.977    0.000    0.192    0.276
##     age_0c           57.733    5.259   10.977    0.000   47.425   68.042
##     bmi_0c           23.638    2.153   10.977    0.000   19.417   27.858
##     painvas_0c        3.971    0.366   10.850    0.000    3.253    4.688
##     poshee_0c         0.757    0.069   10.960    0.000    0.621    0.892
##     swesourceNA_0c    1.351    0.126   10.738    0.000    1.104    1.597
##     fam3_0            0.147    0.013   10.957    0.000    0.121    0.173
## 
## Defined Parameters:
##                    Estimate  Std.Err  z-value  P(>|z|) ci.lower ci.upper
##     ind               0.147    0.693    0.212    0.832   -1.211    1.506
##     total             3.500    3.622    0.966    0.334   -3.599   10.600

MC(model_fit, alpha = 0.05, seed = 1234)

## Monte Carlo Confidence Intervals
##                                       est      se     R     2.5%     97.5%
## a1                                 0.0841  0.3836 20000  -0.6753    0.8329
## a2                                 0.4764  0.0900 20000   0.3007    0.6524
## a3                                -0.0263  0.0094 20000  -0.0446   -0.0078
## a4                                -0.2695  0.4190 20000  -1.0921    0.5541
## a5                                -0.5746  0.4531 20000  -1.4616    0.3228
## a6                                -0.1172  0.3748 20000  -0.8561    0.6145
## a7                                 0.0665  0.0292 20000   0.0093    0.1242
## a8                                -0.0598  0.0364 20000  -0.1307    0.0120
## a9                                -0.0571  0.1069 20000  -0.2680    0.1524
## a10                                0.4548  0.2227 20000   0.0185    0.8902
## a11                               -0.0709  0.2005 20000  -0.4576    0.3203
## a12                                0.0016  0.5278 20000  -1.0363    1.0237
## c1                                 3.3530  3.6291 20000  -3.7550   10.5163
## c2                                 0.6863  1.3792 20000  -2.0027    3.3537
## c3                                 0.4890  0.0860 20000   0.3191    0.6565
## c4                                -4.1995  4.4106 20000 -12.8188    4.4584
## c5                                 1.3696  4.5089 20000  -7.4891   10.2023
## c6                                -2.6247  3.6742 20000  -9.8057    4.6899
## c7                                 0.8321  0.2817 20000   0.2733    1.3867
## c8                                -0.0702  0.3971 20000  -0.8605    0.7020
## c9                                 2.2804  0.9659 20000   0.3937    4.1568
## c10                                2.2042  2.2272 20000  -2.1591    6.5393
## c11                               -2.3511  1.7547 20000  -5.7848    1.0994
## c12                               -1.9187  4.8161 20000 -11.2643    7.5817
## b1                                 1.7506  1.5817 20000  -1.3598    4.8837
## groupfu~~collimpint_0c            -0.0075  0.0923 20000  -0.1899    0.1747
## groupfu~~muskeltraint_0c          -1.3377  0.9422 20000  -3.1870    0.5059
## groupfu~~HIE                       0.0069  0.0153 20000  -0.0232    0.0367
## groupfu~~LIE                      -0.0035  0.0152 20000  -0.0333    0.0263
## groupfu~~sex_0                     0.0003  0.0156 20000  -0.0299    0.0311
## groupfu~~age_0c                   -0.0706  0.2457 20000  -0.5508    0.4103
## groupfu~~bmi_0c                   -0.0398  0.1580 20000  -0.3494    0.2698
## groupfu~~painvas_0c               -0.1537  0.0653 20000  -0.2834   -0.0276
## groupfu~~poshee_0c                 0.0027  0.0282 20000  -0.0521    0.0586
## groupfu~~swesourceNA_0c            0.0697  0.0385 20000  -0.0059    0.1456
## groupfu~~fam3_0                   -0.0327  0.0126 20000  -0.0575   -0.0079
## collimpint_0c~~muskeltraint_0c     5.7663  5.2803 20000  -4.5703   16.0241
## collimpint_0c~~HIE                 0.0638  0.0846 20000  -0.1023    0.2289
## collimpint_0c~~LIE                 0.0165  0.0904 20000  -0.1567    0.1974
## collimpint_0c~~sex_0               0.0427  0.0904 20000  -0.1327    0.2203
## collimpint_0c~~age_0c              0.2518  1.4301 20000  -2.5293    3.0572
## collimpint_0c~~bmi_0c             -0.6363  0.8941 20000  -2.4002    1.0925
## collimpint_0c~~painvas_0c         -0.0083  0.3849 20000  -0.7636    0.7439
## collimpint_0c~~poshee_0c           0.2447  0.1594 20000  -0.0686    0.5583
## collimpint_0c~~swesourceNA_0c     -0.3207  0.2357 20000  -0.7785    0.1388
## collimpint_0c~~fam3_0             -0.0739  0.0825 20000  -0.2352    0.0882
## muskeltraint_0c~~HIE               0.5372  0.8873 20000  -1.2124    2.2657
## muskeltraint_0c~~LIE              -0.2575  0.8923 20000  -2.0038    1.5147
## muskeltraint_0c~~sex_0            -0.9157  0.9153 20000  -2.7294    0.8546
## muskeltraint_0c~~age_0c           11.6413 14.2879 20000 -16.5006   39.6693
## muskeltraint_0c~~bmi_0c          -24.2002  9.2824 20000 -42.4774   -6.2266
## muskeltraint_0c~~painvas_0c       -1.2141  3.7778 20000  -8.5328    6.3721
## muskeltraint_0c~~poshee_0c         2.1536  1.6505 20000  -1.0932    5.3954
## muskeltraint_0c~~swesourceNA_0c   -2.6105  2.2276 20000  -7.0256    1.7367
## muskeltraint_0c~~fam3_0            1.0713  0.7258 20000  -0.3525    2.4892
## HIE~~LIE                          -0.1144  0.0161 20000  -0.1461   -0.0827
## HIE~~sex_0                        -0.0010  0.0147 20000  -0.0303    0.0276
## HIE~~age_0c                        0.1878  0.2311 20000  -0.2611    0.6459
## HIE~~bmi_0c                        0.3432  0.1495 20000   0.0491    0.6329
## HIE~~painvas_0c                   -0.0235  0.0609 20000  -0.1449    0.0948
## HIE~~poshee_0c                     0.0435  0.0268 20000  -0.0085    0.0967
## HIE~~swesourceNA_0c               -0.0357  0.0362 20000  -0.1065    0.0353
## HIE~~fam3_0                        0.0021  0.0117 20000  -0.0208    0.0250
## LIE~~sex_0                         0.0016  0.0149 20000  -0.0277    0.0307
## LIE~~age_0c                       -0.2255  0.2314 20000  -0.6763    0.2272
## LIE~~bmi_0c                       -0.3705  0.1505 20000  -0.6668   -0.0786
## LIE~~painvas_0c                    0.0355  0.0616 20000  -0.0854    0.1558
## LIE~~poshee_0c                    -0.0395  0.0266 20000  -0.0920    0.0127
## LIE~~swesourceNA_0c                0.0092  0.0361 20000  -0.0623    0.0798
## LIE~~fam3_0                       -0.0063  0.0118 20000  -0.0291    0.0168
## sex_0~~age_0c                      0.0533  0.2372 20000  -0.4016    0.5179
## sex_0~~bmi_0c                     -0.1011  0.1535 20000  -0.4023    0.2032
## sex_0~~painvas_0c                 -0.0070  0.0628 20000  -0.1304    0.1139
## sex_0~~poshee_0c                  -0.0334  0.0272 20000  -0.0868    0.0199
## sex_0~~swesourceNA_0c             -0.0753  0.0370 20000  -0.1489   -0.0029
## sex_0~~fam3_0                     -0.0254  0.0122 20000  -0.0493   -0.0013
## age_0c~~bmi_0c                    -1.6174  2.3964 20000  -6.3505    3.0701
## age_0c~~painvas_0c                -2.7606  0.9963 20000  -4.7085   -0.8091
## age_0c~~poshee_0c                 -1.3044  0.4345 20000  -2.1543   -0.4584
## age_0c~~swesourceNA_0c            -1.0974  0.5823 20000  -2.2608    0.0217
## age_0c~~fam3_0                    -0.0283  0.1878 20000  -0.3976    0.3329
## bmi_0c~~painvas_0c                 1.8250  0.6468 20000   0.5486    3.1004
## bmi_0c~~poshee_0c                  0.0606  0.2734 20000  -0.4739    0.5923
## bmi_0c~~swesourceNA_0c             0.6518  0.3731 20000  -0.0717    1.3906
## bmi_0c~~fam3_0                     0.0562  0.1209 20000  -0.1809    0.2927
## painvas_0c~~poshee_0c             -0.0248  0.1198 20000  -0.2584    0.2110
## painvas_0c~~swesourceNA_0c         0.3537  0.1518 20000   0.0544    0.6513
## painvas_0c~~fam3_0                 0.0624  0.0511 20000  -0.0377    0.1626
## poshee_0c~~swesourceNA_0c         -0.1233  0.0670 20000  -0.2559    0.0070
## poshee_0c~~fam3_0                  0.0029  0.0217 20000  -0.0399    0.0456
## swesourceNA_0c~~fam3_0             0.0055  0.0293 20000  -0.0515    0.0636
## collimpint_18c~~collimpint_18c     2.2700  0.3870 20000   1.5129    3.0271
## muskeltraint_24~~muskeltraint_24 506.2412 55.8804 20000 395.7296  614.2930
## groupfu~~groupfu                   0.2499  0.0227 20000   0.2060    0.2949
## collimpint_0c~~collimpint_0c       4.4313  0.5564 20000   3.3293    5.5259
## muskeltraint_0c~~muskeltraint_0c 850.7022 77.2481 20000 701.3594 1003.0107
## HIE~~HIE                           0.2231  0.0202 20000   0.1830    0.2624
## LIE~~LIE                           0.2245  0.0203 20000   0.1843    0.2639
## sex_0~~sex_0                       0.2340  0.0213 20000   0.1927    0.2763
## age_0c~~age_0c                    57.7334  5.1929 20000  47.6151   67.8015
## bmi_0c~~bmi_0c                    23.6379  2.1596 20000  19.4576   27.9549
## painvas_0c~~painvas_0c             3.9705  0.3664 20000   3.2624    4.6916
## poshee_0c~~poshee_0c               0.7568  0.0695 20000   0.6190    0.8921
## swesourceNA_0c~~swesourceNA_0c     1.3505  0.1256 20000   1.1046    1.5979
## fam3_0~~fam3_0                     0.1470  0.0135 20000   0.1209    0.1737
## collimpint_18c~1                   0.1677  0.3656 20000  -0.5495    0.8800
## muskeltraint_24~1                 23.6907  3.9218 20000  15.8099   31.3735
## groupfu~1                          0.5104  0.0322 20000   0.4477    0.5738
## collimpint_0c~1                   -0.1591  0.1868 20000  -0.5227    0.2078
## muskeltraint_0c~1                  0.0000  1.8675 20000  -3.6715    3.6007
## HIE~1                              0.3361  0.0302 20000   0.2778    0.3956
## LIE~1                              0.3402  0.0308 20000   0.2793    0.3999
## sex_0~1                            0.3734  0.0312 20000   0.3129    0.4346
## age_0c~1                           0.0000  0.4891 20000  -0.9678    0.9505
## bmi_0c~1                           0.0000  0.3152 20000  -0.6138    0.6201
## painvas_0c~1                      -0.0105  0.1297 20000  -0.2619    0.2464
## poshee_0c~1                       -0.0004  0.0562 20000  -0.1097    0.1112
## swesourceNA_0c~1                  -0.0012  0.0764 20000  -0.1532    0.1479
## fam3_0~1                           0.1791  0.0247 20000   0.1309    0.2277
## ind                                0.1473  0.9306 20000  -1.6443    2.3928
## total                              3.5003  3.6757 20000  -3.5760   10.7510

S-Figure 4

MVPA: Intervention Condition

### Individual trajectories
plot_mvpaagt_int <- ggplot(long3[long3$groupfu == 1, ], aes(x = time0, y = mvpaagt)) + 
  geom_line() + guides(colour = "none") + 
  aes(colour = factor(id)) + 
  xlab("") + 
  ylab ("") + 
  scale_x_continuous(breaks = seq(0,24,6), labels = NULL) + 
  scale_y_continuous(breaks = seq(0,150,20), minor_breaks = NULL) + 
  theme_light() + 
  theme(axis.ticks.x = element_blank()) 

### Time trend from multilevel analysis (SPSS)
f_mvpaagt_int <- function(x) 46.03464508593866-0.291295363658579*x 

### Add time trend to plot
plot_mvpaagt_int2 <- plot_mvpaagt_int + stat_function(fun = f_mvpaagt_int, colour = "black", linewidth = 1)

MVPA: Control Condition

### Individual trajectories
plot_mvpaagt_ctrl <- ggplot(long3[long3$groupfu == 0, ], aes(x = time0, y = mvpaagt)) + 
  geom_line() + guides(colour = "none") + 
  aes(colour = factor(id)) + 
  xlab("") + 
  ylab ("") + 
  scale_x_continuous(breaks = seq(0,24,6), labels = NULL) + 
  scale_y_continuous(breaks = seq(0,150,20), minor_breaks = NULL) + 
  theme_light() + 
  theme(axis.ticks.x = element_blank()) + 
  theme(axis.ticks.y = element_blank())

### Time trend from multilevel analysis (SPSS)
f_mvpaagt_ctrl <- function(x) 49.56379926072232-0.448076513948185*x 

### Add time trend to plot
plot_mvpaagt_ctrl2 <- plot_mvpaagt_ctrl + stat_function(fun = f_mvpaagt_ctrl, colour = "black", linewidth = 1)

Muscle Strength Training: Intervention Condition

### Individual trajectories
plot_muskeltraint_int <- ggplot(long5[long5$groupfu == 1, ], aes(x = time0, y = muskeltraint)) + 
  geom_line() + guides(colour = "none") + 
  aes(colour = factor(id)) + 
  xlab("") + 
  ylab ("") + 
  scale_x_continuous(breaks = seq(0,24,6), labels = NULL) + 
  scale_y_continuous(breaks = seq(0,120,20), minor_breaks = NULL) + 
  theme_light() + 
  theme(axis.ticks.x = element_blank()) 

### Time trend from multilevel analysis (SPSS)
f_muskeltraint_int <- function(x) 16.096236383738976 +  0.306221289844427 *x 

### Add time trend to plot
plot_muskeltraint_int2 <- plot_muskeltraint_int + stat_function(fun = f_muskeltraint_int, colour = "black", linewidth = 1)

Muscle Strength Training: Control Condition

### Individual trajectories
plot_muskeltraint_ctrl <- ggplot(long5[long5$groupfu == 0, ], aes(x = time0, y = muskeltraint)) + 
  geom_line() + guides(colour = "none") + 
  aes(colour = factor(id)) + 
  xlab("") + 
  ylab ("") + 
  scale_x_continuous(breaks = seq(0,24,6), labels = NULL) + 
  scale_y_continuous(breaks = seq(0,120,20), minor_breaks = NULL) + 
  theme_light() + 
  theme(axis.ticks.x = element_blank()) + 
  theme(axis.ticks.y = element_blank())

### Time trend from multilevel analysis (SPSS)
f_muskeltraint_ctrl <- function(x) 15.534208142970801 + 0.217075293873867*x 

### Add time trend to plot
plot_muskeltraint_ctrl2 <- plot_muskeltraint_ctrl + stat_function(fun = f_muskeltraint_ctrl, colour = "black", linewidth = 1)

Action Planning: Intervention Condition

### Individual trajectories
plot_acplan_int <- ggplot(long5[long5$groupfu == 1, ], aes(x = time0, y = acplan)) + 
  geom_line() + guides(colour = "none") + 
  aes(colour = factor(id)) + 
  xlab("") + 
  ylab ("") + 
  scale_x_continuous(breaks = seq(0,24,6), labels = NULL) + 
  scale_y_continuous(breaks = seq(1,6,1), minor_breaks = NULL) + 
  theme_light() + 
  theme(axis.ticks.x = element_blank())

### Time trend from multilevel analysis (SPSS)
f_acplan_int <- function(x) 4.221770916813307+0.011975369682185*x 

### Add time trend to plot
plot_acplan_int2 <- plot_acplan_int + stat_function(fun = f_acplan_int, colour = "black", linewidth = 1)

Action Planning: Control Condition

### Individual trajectories
plot_acplan_ctrl <- ggplot(long5[long5$groupfu == 0, ], aes(x = time0, y = acplan)) + 
  geom_line() + guides(colour = "none") + 
  aes(colour = factor(id)) + 
  xlab("") + 
  ylab ("") + 
  scale_x_continuous(breaks = seq(0,24,6), labels = NULL) + 
  scale_y_continuous(breaks = seq(1,6,1), minor_breaks = NULL) + 
  theme_light() + 
  theme(axis.ticks.x = element_blank()) + 
  theme(axis.ticks.y = element_blank())

### Time trend from multilevel analysis (SPSS)
f_acplan_ctrl <- function(x) 4.105929994249274-0.009864604534969*x 

### Add time trend to plot
plot_acplan_ctrl2 <- plot_acplan_ctrl + stat_function(fun = f_acplan_ctrl, colour = "black", linewidth = 1)

Coping Planning: Intervention Condition

### Individual trajectories
plot_coplan_int <- ggplot(long5[long5$groupfu == 1, ], aes(x = time0, y = coplan)) + 
  geom_line() + guides(colour = "none") + 
  aes(colour = factor(id)) + 
  xlab("") + 
  ylab ("") + 
  scale_x_continuous(breaks = seq(0,24,6), labels = NULL) + 
  scale_y_continuous(breaks = seq(1,6,1), minor_breaks = NULL) + 
  theme_light() + 
  theme(axis.ticks.x = element_blank())

### Time trend from multilevel analysis (SPSS)
f_coplan_int <- function(x) 3.131637814029704+0.009820518403709*x 

### Add time trend to plot
plot_coplan_int2 <- plot_coplan_int + stat_function(fun = f_coplan_int, colour = "black", linewidth = 1)

Coping Planning: Control Condition

### Individual trajectories
plot_coplan_ctrl <- ggplot(long5[long5$groupfu == 0, ], aes(x = time0, y = coplan)) + 
  geom_line() + guides(colour = "none") + 
  aes(colour = factor(id)) + 
  xlab("") + 
  ylab ("") + 
  scale_x_continuous(breaks = seq(0,24,6), labels = NULL) + 
  scale_y_continuous(breaks = seq(1,6,1), minor_breaks = NULL) + 
  theme_light() + 
  theme(axis.ticks.x = element_blank()) + 
  theme(axis.ticks.y = element_blank())

### Time trend from multilevel analysis (SPSS)
f_coplan_ctrl <- function(x) 3.179038031777823+0.006954499696008*x 

### Add time trend to plot
plot_coplan_ctrl2 <- plot_coplan_ctrl + stat_function(fun = f_coplan_ctrl, colour = "black", linewidth = 1)

Maintenance Self-Efficacy: Intervention Condition

### Individual trajectories
plot_aufswe_int <- ggplot(long5[long5$groupfu == 1, ], aes(x = time0, y = aufswe)) + 
  geom_line() + guides(colour = "none") + 
  aes(colour = factor(id)) + 
  xlab("") + 
  ylab ("") + 
  scale_x_continuous(breaks = seq(0,24,6), labels = NULL) + 
  scale_y_continuous(breaks = seq(1,6,1), minor_breaks = NULL) + 
  theme_light() + 
  theme(axis.ticks.x = element_blank())

### Time trend from multilevel analysis (SPSS)
f_aufswe_int <- function(x) 4.294648930224217-0.004710128473129*x 

### Add time trend to plot
plot_aufswe_int2 <- plot_aufswe_int + stat_function(fun = f_aufswe_int, colour = "black", linewidth = 1)

Maintenance Self-Efficacy: Control Condition

### Individual trajectories
plot_aufswe_ctrl <- ggplot(long5[long5$groupfu == 0, ], aes(x = time0, y = aufswe)) + 
  geom_line() + guides(colour = "none") + 
  aes(colour = factor(id)) + 
  xlab("") + 
  ylab ("") + 
  scale_x_continuous(breaks = seq(0,24,6), labels = NULL) + 
  scale_y_continuous(breaks = seq(1,6,1), minor_breaks = NULL) + 
  theme_light() + 
  theme(axis.ticks.x = element_blank()) + 
  theme(axis.ticks.y = element_blank())

### Time trend from multilevel analysis (SPSS)
f_aufswe_ctrl <- function(x) 4.538196101654467-0.019506866842913*x 

### Add time trend to plot
plot_aufswe_ctrl2 <- plot_aufswe_ctrl + stat_function(fun = f_aufswe_ctrl, colour = "black", linewidth = 1)

Recovery Self-Efficacy: Intervention Condition

### Individual trajectories
plot_wieswe_int <- ggplot(long5[long5$groupfu == 1, ], aes(x = time0, y = wieswe)) + 
  geom_line() + guides(colour = "none") + 
  aes(colour = factor(id)) + 
  xlab("") + 
  ylab ("") + 
  scale_x_continuous(breaks = seq(0,24,6), labels = NULL) + 
  scale_y_continuous(breaks = seq(1,6,1), minor_breaks = NULL) + 
  theme_light() + 
  theme(axis.ticks.x = element_blank()) 

### Time trend from multilevel analysis (SPSS)
f_wieswe_int <- function(x) 4.879895418258709-0.003528651653626*x 

### Add time trend to plot
plot_wieswe_int2 <- plot_wieswe_int + stat_function(fun = f_wieswe_int, colour = "black", linewidth = 1)

Recovery Self-Efficacy: Control Condition

### Individual trajectories
plot_wieswe_ctrl <- ggplot(long5[long5$groupfu == 0, ], aes(x = time0, y = wieswe)) + 
  geom_line() + guides(colour = "none") + 
  aes(colour = factor(id)) + 
  xlab("") + 
  ylab ("") + 
  scale_x_continuous(breaks = seq(0,24,6), labels = NULL) + 
  scale_y_continuous(breaks = seq(1,6,1), minor_breaks = NULL) + 
  theme_light() + 
  theme(axis.ticks.x = element_blank()) + 
  theme(axis.ticks.y = element_blank())

### Time trend from multilevel analysis (SPSS)
f_wieswe_ctrl <- function(x) 5.099219466496593-0.019618342536335*x

### Add time trend to plot
plot_wieswe_ctrl2 <- plot_wieswe_ctrl + stat_function(fun = f_wieswe_ctrl, colour = "black", linewidth = 1)

Action Control: Intervention Condition

### Individual trajectories
plot_hk_int <- ggplot(long5[long5$groupfu == 1, ], aes(x = time0, y = hk)) + 
  geom_line() + guides(colour = "none") + 
  aes(colour = factor(id)) + 
  xlab("") + 
  ylab ("") + 
  scale_x_continuous(breaks = seq(0,24,6), labels = NULL) + 
  scale_y_continuous(breaks = seq(1,6,1), minor_breaks = NULL) + 
  theme_light() + 
  theme(axis.ticks.x = element_blank())

### Time trend from multilevel analysis (SPSS)
f_hk_int <- function(x) 3.359242747498863+0.011477147737352*x 

### Add time trend to plot
plot_hk_int2 <- plot_hk_int + stat_function(fun = f_hk_int, colour = "black", linewidth = 1)

Action Control: Control Condition

### Individual trajectories
plot_hk_ctrl <- ggplot(long5[long5$groupfu == 0, ], aes(x = time0, y = hk)) + 
  geom_line() + guides(colour = "none") + 
  aes(colour = factor(id)) + 
  xlab("") + 
  ylab ("") + 
  scale_x_continuous(breaks = seq(0,24,6), labels = NULL) + 
  scale_y_continuous(breaks = seq(1,6,1), minor_breaks = NULL) + 
  theme_light() + 
  theme(axis.ticks.x = element_blank()) + 
  theme(axis.ticks.y = element_blank())

### Time trend from multilevel analysis (SPSS)
f_hk_ctrl <- function(x) 3.461150797502666-0.000711735115427*x 

### Add time trend to plot
plot_hk_ctrl2 <- plot_hk_ctrl + stat_function(fun = f_hk_ctrl, colour = "black", linewidth = 1)

Collaborative Planning: Intervention Condition

### Individual trajectories
plot_collimpint_int <- ggplot(long5[long5$groupfu == 1, ], aes(x = time0, y = collimpint)) + 
  geom_line() + guides(colour = "none") + 
  aes(colour = factor(id)) + 
  xlab("") + 
  ylab ("") + 
  scale_x_continuous(breaks = seq(0,24,6)) + 
  scale_y_continuous(breaks = seq(1,6,1), minor_breaks = NULL) + 
  theme_light() + 
  theme(axis.ticks.x = element_blank()) 

### Time trend from multilevel analysis (SPSS)
f_collimpint_int <- function(x) 3.28307711533585+0.011265060265051*x 

### Add time trend to plot
plot_collimpint_int2 <- plot_collimpint_int + stat_function(fun = f_collimpint_int, colour = "black", linewidth = 1)

Collaborative Planning: Control Condition

### Individual trajectories
plot_collimpint_ctrl <- ggplot(long5[long5$groupfu == 0, ], aes(x = time0, y = collimpint)) + 
  geom_line() + guides(colour = "none") + 
  aes(colour = factor(id)) + 
  xlab("") + 
  ylab ("") + 
  scale_x_continuous(breaks = seq(0,24,6)) + 
  scale_y_continuous(breaks = seq(1,6,1), minor_breaks = NULL) + 
  theme_light() + 
  theme(axis.ticks.x = element_blank()) + 
  theme(axis.ticks.y = element_blank())

### Time trend from multilevel analysis (SPSS)
f_collimpint_ctrl <- function(x) 3.054214355708105+0.007905672391317*x 

### Add time trend to plot
plot_collimpint_ctrl2 <- plot_collimpint_ctrl + stat_function(fun = f_collimpint_ctrl, colour = "black", linewidth = 1)