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Review of guidance papers on regression modeling in statistical series of medical journals
Although regression models play a central role in the analysis of medical research projects, there still exist many misconceptions on various aspects of modeling leading to faulty analyses. Indeed, the rapidly developing statistical methodology and its recent advances in regression modeling do not s...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8786189/ https://www.ncbi.nlm.nih.gov/pubmed/35073384 http://dx.doi.org/10.1371/journal.pone.0262918 |
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author | Wallisch, Christine Bach, Paul Hafermann, Lorena Klein, Nadja Sauerbrei, Willi Steyerberg, Ewout W. Heinze, Georg Rauch, Geraldine |
author_facet | Wallisch, Christine Bach, Paul Hafermann, Lorena Klein, Nadja Sauerbrei, Willi Steyerberg, Ewout W. Heinze, Georg Rauch, Geraldine |
author_sort | Wallisch, Christine |
collection | PubMed |
description | Although regression models play a central role in the analysis of medical research projects, there still exist many misconceptions on various aspects of modeling leading to faulty analyses. Indeed, the rapidly developing statistical methodology and its recent advances in regression modeling do not seem to be adequately reflected in many medical publications. This problem of knowledge transfer from statistical research to application was identified by some medical journals, which have published series of statistical tutorials and (shorter) papers mainly addressing medical researchers. The aim of this review was to assess the current level of knowledge with regard to regression modeling contained in such statistical papers. We searched for target series by a request to international statistical experts. We identified 23 series including 57 topic-relevant articles. Within each article, two independent raters analyzed the content by investigating 44 predefined aspects on regression modeling. We assessed to what extent the aspects were explained and if examples, software advices, and recommendations for or against specific methods were given. Most series (21/23) included at least one article on multivariable regression. Logistic regression was the most frequently described regression type (19/23), followed by linear regression (18/23), Cox regression and survival models (12/23) and Poisson regression (3/23). Most general aspects on regression modeling, e.g. model assumptions, reporting and interpretation of regression results, were covered. We did not find many misconceptions or misleading recommendations, but we identified relevant gaps, in particular with respect to addressing nonlinear effects of continuous predictors, model specification and variable selection. Specific recommendations on software were rarely given. Statistical guidance should be developed for nonlinear effects, model specification and variable selection to better support medical researchers who perform or interpret regression analyses. |
format | Online Article Text |
id | pubmed-8786189 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-87861892022-01-25 Review of guidance papers on regression modeling in statistical series of medical journals Wallisch, Christine Bach, Paul Hafermann, Lorena Klein, Nadja Sauerbrei, Willi Steyerberg, Ewout W. Heinze, Georg Rauch, Geraldine PLoS One Research Article Although regression models play a central role in the analysis of medical research projects, there still exist many misconceptions on various aspects of modeling leading to faulty analyses. Indeed, the rapidly developing statistical methodology and its recent advances in regression modeling do not seem to be adequately reflected in many medical publications. This problem of knowledge transfer from statistical research to application was identified by some medical journals, which have published series of statistical tutorials and (shorter) papers mainly addressing medical researchers. The aim of this review was to assess the current level of knowledge with regard to regression modeling contained in such statistical papers. We searched for target series by a request to international statistical experts. We identified 23 series including 57 topic-relevant articles. Within each article, two independent raters analyzed the content by investigating 44 predefined aspects on regression modeling. We assessed to what extent the aspects were explained and if examples, software advices, and recommendations for or against specific methods were given. Most series (21/23) included at least one article on multivariable regression. Logistic regression was the most frequently described regression type (19/23), followed by linear regression (18/23), Cox regression and survival models (12/23) and Poisson regression (3/23). Most general aspects on regression modeling, e.g. model assumptions, reporting and interpretation of regression results, were covered. We did not find many misconceptions or misleading recommendations, but we identified relevant gaps, in particular with respect to addressing nonlinear effects of continuous predictors, model specification and variable selection. Specific recommendations on software were rarely given. Statistical guidance should be developed for nonlinear effects, model specification and variable selection to better support medical researchers who perform or interpret regression analyses. Public Library of Science 2022-01-24 /pmc/articles/PMC8786189/ /pubmed/35073384 http://dx.doi.org/10.1371/journal.pone.0262918 Text en © 2022 Wallisch et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wallisch, Christine Bach, Paul Hafermann, Lorena Klein, Nadja Sauerbrei, Willi Steyerberg, Ewout W. Heinze, Georg Rauch, Geraldine Review of guidance papers on regression modeling in statistical series of medical journals |
title | Review of guidance papers on regression modeling in statistical series of medical journals |
title_full | Review of guidance papers on regression modeling in statistical series of medical journals |
title_fullStr | Review of guidance papers on regression modeling in statistical series of medical journals |
title_full_unstemmed | Review of guidance papers on regression modeling in statistical series of medical journals |
title_short | Review of guidance papers on regression modeling in statistical series of medical journals |
title_sort | review of guidance papers on regression modeling in statistical series of medical journals |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8786189/ https://www.ncbi.nlm.nih.gov/pubmed/35073384 http://dx.doi.org/10.1371/journal.pone.0262918 |
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