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Review
. 2016 May;95(20):e3653.
doi: 10.1097/MD.0000000000003653.

Quality Reporting of Multivariable Regression Models in Observational Studies: Review of a Representative Sample of Articles Published in Biomedical Journals

Jordi Real  1 , Carles Forné, Albert Roso-Llorach, Jose M Martínez-Sánchez
Affiliations
Review

Quality Reporting of Multivariable Regression Models in Observational Studies: Review of a Representative Sample of Articles Published in Biomedical Journals

Jordi Real et al. Medicine (Baltimore). 2016 May.

Abstract

Controlling for confounders is a crucial step in analytical observational studies, and multivariable models are widely used as statistical adjustment techniques. However, the validation of the assumptions of the multivariable regression models (MRMs) should be made clear in scientific reporting. The objective of this study is to review the quality of statistical reporting of the most commonly used MRMs (logistic, linear, and Cox regression) that were applied in analytical observational studies published between 2003 and 2014 by journals indexed in MEDLINE.Review of a representative sample of articles indexed in MEDLINE (n = 428) with observational design and use of MRMs (logistic, linear, and Cox regression). We assessed the quality of reporting about: model assumptions and goodness-of-fit, interactions, sensitivity analysis, crude and adjusted effect estimate, and specification of more than 1 adjusted model.The tests of underlying assumptions or goodness-of-fit of the MRMs used were described in 26.2% (95% CI: 22.0-30.3) of the articles and 18.5% (95% CI: 14.8-22.1) reported the interaction analysis. Reporting of all items assessed was higher in articles published in journals with a higher impact factor.A low percentage of articles indexed in MEDLINE that used multivariable techniques provided information demonstrating rigorous application of the model selected as an adjustment method. Given the importance of these methods to the final results and conclusions of observational studies, greater rigor is required in reporting the use of MRMs in the scientific literature.

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Conflict of interest statement

The authors have no conflicts of interest to disclose.

Figures

FIGURE 1
FIGURE 1
Flowchart of articles included.
FIGURE 2
FIGURE 2
Relative frequency for each item searched (A), mean number of items per article (i.e., application of a multivariable regression model, stratified by impact factor and by sample size) (B), study design, (C) and type of model used (D).
See this image and copyright information in PMC

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