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Quantitative investigation of inappropriate regression model construction and the importance of medical statistics experts in observational medical research: a cross-sectional study
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    Discussion of “A quantitative investigation of inappropriate use of model selection and the importance of medical statistics experts in observational medical research: a cross-sectional study ” by Nojima, Tokunaga and Nagamura
    • Anthony Atkinson, Professor, Department of Statistics London School of Economics

    The problem is the method used in practice for the selection of variables to be included in logistic regression and Cox models in observational medical studies.

    The motivation comes from the authors’ work as statistical consultants. Many medical researchers had the idea that only variables which were individually significant should be included in the fitted model. This is in contrast to the correct procedure in which the model should contain variables that are jointly significant. To find these models requires fitting several models and selecting the best, rather than fitting just one. An example is in Table 1 below.

    The paper presents the results of a survey in which the frequency of an incorrect method of variable selection was measured as a function of the assessed statistical expertise of the authors of the papers: first author, any other author or none. The expertise was based on the authors’ departmental affiliations. It was found that the frequency of correct variable selection increased with the statistical qualifications of the authors. Clinical trials, as opposed to observational studies, were not included.

    The authors also consider how the situation might be improved. A breakdown of the results by country from papers in which the first author is not an expert shows North America and Northern Europe show relatively high expert involvement compared with East Asia, which have a lower involvement. Taiwan is an exception. In the authors’ own cou...

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    Conflict of Interest:
    None declared.