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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
  1. Masanori Nojima1,2,
  2. Mutsumi Tokunaga1,3,
  3. Fumitaka Nagamura1,2
  1. 1 Center for Translational Research, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
  2. 2 The Division of Advanced Medicine Promotion, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
  3. 3 Department of Health and Social Behavior, School of Public Health, The University of Tokyo, Tokyo, Japan
  1. Correspondence to Dr Masanori Nojima; nojima{at}ims.u-tokyo.ac.jp

Abstract

Objective To investigate under what circumstances inappropriate use of ‘multivariate analysis’ is likely to occur and to identify the population that needs more support with medical statistics.

Study design and settings The frequency of inappropriate regression model construction in multivariate analysis and related factors were investigated in observational medical research publications.

Results The inappropriate algorithm of using only variables that were significant in univariate analysis was estimated to occur at 6.4% (95% CI 4.8% to 8.5%). This was observed in 1.1% of the publications with a medical statistics expert (hereinafter ‘expert’) as the first author, 3.5% if an expert was included as coauthor and in 12.2% if experts were not involved. In the publications where the number of cases was 50 or less and the study did not include experts, inappropriate algorithm usage was observed with a high proportion of 20.2%. The OR of the involvement of experts for this outcome was 0.28 (95% CI 0.15 to 0.53). A further, nation-level, analysis showed that the involvement of experts and the implementation of unfavourable multivariate analysis are associated at the nation-level analysis (R=−0.652).

Conclusion Based on the results of this study, the benefit of participation of medical statistics experts is obvious. Experts should be involved for proper confounding adjustment and interpretation of statistical models.

  • multivariate analysis
  • regression analysis
  • biostatistics
  • clinical research
  • observational research
  • medical statistics expert

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

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Footnotes

  • Contributors MN: conception and design of the study, writing the manuscript, analysis and interpretation of data. MT: acquisition and interpretation of data and critical revision of the manuscript. FN: supervising the overall research and critical revision of the manuscript.

  • Funding This study was supported by grants-in-aid for scientific research (C), JSPS KAKENHI grant number JP 26460764 (fiscal-year 2014-2016, Masanori Nojima).

  • Competing interests None declared.

  • Patient consent Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement No additional data are available.

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