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Systematic review of education and practical guidance on regression modeling for medical researchers who lack a strong statistical background: Study protocol

Author

Listed:
  • Paul Bach
  • Christine Wallisch
  • Nadja Klein
  • Lorena Hafermann
  • Willi Sauerbrei
  • Ewout W Steyerberg
  • Georg Heinze
  • Geraldine Rauch
  • for topic group 2 of the STRATOS initiative

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.

Suggested Citation

  • Paul Bach & Christine Wallisch & Nadja Klein & Lorena Hafermann & Willi Sauerbrei & Ewout W Steyerberg & Georg Heinze & Geraldine Rauch & for topic group 2 of the STRATOS initiative, 2020. "Systematic review of education and practical guidance on regression modeling for medical researchers who lack a strong statistical background: Study protocol," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-10, December.
  • Handle: RePEc:plo:pone00:0241427
    DOI: 10.1371/journal.pone.0241427
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    References listed on IDEAS

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    1. Walter Bouwmeester & Nicolaas P A Zuithoff & Susan Mallett & Mirjam I Geerlings & Yvonne Vergouwe & Ewout W Steyerberg & Douglas G Altman & Karel G M Moons, 2012. "Reporting and Methods in Clinical Prediction Research: A Systematic Review," PLOS Medicine, Public Library of Science, vol. 9(5), pages 1-13, May.
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    Cited by:

    1. Christine Wallisch & Paul Bach & Lorena Hafermann & Nadja Klein & Willi Sauerbrei & Ewout W Steyerberg & Georg Heinze & Geraldine Rauch & on behalf of topic group 2 of the STRATOS initiative, 2022. "Review of guidance papers on regression modeling in statistical series of medical journals," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-20, January.

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