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Title:
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Quality Reporting of Multivariable Regression Models in Observational Studies
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Author:
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Real, Jordi; Forné Izquierdo, Carles; Roso-Llorach, Albert; Martínez Sánchez, Jose M.
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Notes:
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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
(n¼428) with observational design and use ofMRMs (logistic, linear, and
Cox regression).We assessedthe 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 assessedwas higher in articles published in journalswith 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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Rights:
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cc-by (c) Wolters Kluwer Health, Inc., 2016
http://creativecommons.org/licenses/by/3.0/es/
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Document type:
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article
publishedVersion |
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Published by:
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Lippincott, Williams & Wilkins
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