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

Paul Bach,Christine Wallisch,Nadja Klein,Willi Sauerbrei,For Topic Group 2 Of The Stratos Initiative ,Georg Heinze,Lorena Hafermann,Geraldine Rauch,Ewout W Steyerberg

doi:10.1371/journal.pone.0241427

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.

Highlights

RationaleIn the last decades, intensive global research activities led to a huge general medical progress
This is the 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
Material and methods Eligibility criteria. This is a comprehensive review to identify topic-relevant articles on regression modeling within statistical series published in medical journals, which are defined as journals with a Systematic review of guidance for regression modeling: study protocol target audience mainly or exclusively consisting of medical researchers or practitioners

Summary

Introduction

RationaleIn the last decades, intensive global research activities led to a huge general medical progress. One reason is that many statistical analyses are not conducted by professional experts but by researchers with limited statistical background to reduce time or financial resources or because a professional statistician is not available. Such researchers cannot be aware of all statistical pitfalls and will usually not overview the latest developments in statistical methodology, which is already a challenge for a professional biostatistician. Appropriate guidance and tutorials are often missing for medical researchers with a limited background in statistical methodology as the available statistical articles are often written in a rather technical manner

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Methods

Conclusion