dc.contributor.author
Bach, Paul
dc.contributor.author
Wallisch, Christine
dc.contributor.author
Klein, Nadja
dc.contributor.author
Hafermann, Lorena
dc.contributor.author
Sauerbrei, Willi
dc.contributor.author
Steyerberg, Ewout W.
dc.contributor.author
Heinze, Georg
dc.contributor.author
Rauch, Geraldine
dc.contributor.author
Topic group 2 of the STRATOS initiative
dc.date.accessioned
2021-04-15T11:23:28Z
dc.date.available
2021-04-15T11:23:28Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/30362
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-30103
dc.description.abstract
In the last decades, statistical methodology has developed rapidly, in particular in the field of regression modeling. Multivariable regression models are applied in almost all medical research projects. Therefore, the potential impact of statistical misconceptions within this field can be enormous Indeed, the current theoretical statistical knowledge is not always adequately transferred to the current practice in medical statistics. Some medical journals have identified this problem and published isolated statistical articles and even whole series thereof. In this systematic review, we aim to assess the current level of education on regression modeling that is provided to medical researchers via series of statistical articles published in medical journals. The present manuscript is a protocol for a systematic review that aims to assess which aspects of regression modeling are covered by statistical series published in medical journals that intend to train and guide applied medical researchers with limited statistical knowledge. Statistical paper series cannot easily be summarized and identified by common keywords in an electronic search engine like Scopus. We therefore identified series by a systematic request to statistical experts who are part or related to the STRATOS Initiative (STRengthening Analytical Thinking for Observational Studies). Within each identified article, two raters will independently check the content of the articles with respect to a predefined list of key aspects related to regression modeling. The content analysis of the topic-relevant articles will be performed using a predefined report form to assess the content as objectively as possible. Any disputes will be resolved by a third reviewer. Summary analyses will identify potential methodological gaps and misconceptions that may have an important impact on the quality of analyses in medical research. This review will thus provide a basis for future guidance papers and tutorials in the field of regression modeling which will enable medical researchers 1) to interpret publications in a correct way, 2) to perform basic statistical analyses in a correct way and 3) to identify situations when the help of a statistical expert is required.
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
Data Collection
en
dc.subject
Models, Statistical
en
dc.subject
Observational Studies as Topic
en
dc.subject
Periodicals as Topic
en
dc.subject
Regression Analysis
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
Systematic review of education and practical guidance on regression modeling for medical researchers who lack a strong statistical background: Study protocol
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
e0241427
dcterms.bibliographicCitation.doi
10.1371/journal.pone.0241427
dcterms.bibliographicCitation.journaltitle
PLOS ONE
dcterms.bibliographicCitation.number
12
dcterms.bibliographicCitation.originalpublishername
Public Library of Science (PLoS)
dcterms.bibliographicCitation.volume
15
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
33347441
dcterms.isPartOf.eissn
1932-6203