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dc.contributor.authorReal, Jordi
dc.contributor.authorForné Izquierdo, Carles
dc.contributor.authorRoso-Llorach, Albert
dc.contributor.authorMartínez Sánchez, Jose M.
dc.description.abstractControlling 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.ca_ES
dc.publisherLippincott, Williams & Wilkinsca_ES
dc.relation.isformatofReproducció del document publicat a
dc.relation.ispartofMedicine, 2016, vol. 95, núm. 20, e3653ca_ES
dc.rightscc-by (c) Wolters Kluwer Health, Inc., 2016ca_ES
dc.titleQuality Reporting of Multivariable Regression Models in Observational Studiesca_ES

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cc-by (c) Wolters Kluwer Health, Inc., 2016
Except where otherwise noted, this item's license is described as cc-by (c) Wolters Kluwer Health, Inc., 2016