Logistic regression in the medical literature:: Standards for use and reporting, with particular attention to one medical domain

Logistic regression (LR) is a widely used multivariable method for modeling dichotomous outcomes. This article examines use and reporting of LR in the medical literature by comprehensively assessing its use in a selected area of medical study. Medline, followed by bibliography searches, identified 1...

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Bibliographic Details
Published in:Journal of clinical epidemiology 2001-10, Vol.54 (10), p.979-985
Main Authors: Bagley, Steven C, White, Halbert, Golomb, Beatrice A
Format: Article
Language:English
Subjects:
Biological and medical sciences
Cancer susceptibility
Epidemiologic Studies
Epidemiology
Genetic testing
Genetic Testing - standards
Guideline Adherence
Humans
Logistic Models
Logistic regression
Medical sciences
Neoplasms - epidemiology
Neoplasms - genetics
Publishing - standards
Regression analysis
Reproducibility of Results
Statistical methods
Tumors
Validation
Citations: Items that cite this one
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Summary:Logistic regression (LR) is a widely used multivariable method for modeling dichotomous outcomes. This article examines use and reporting of LR in the medical literature by comprehensively assessing its use in a selected area of medical study. Medline, followed by bibliography searches, identified 15 peer-reviewed English-language articles with original data, employing LR, published between 1985 and 1999, pertaining to patient interest in genetic testing for cancer susceptibility. Articles were examined for each of 10 criteria for proper use and reporting of LR models. Substantial shortcomings were found in both use of LR and reporting of results. For many studies, the ratio of the number of outcome events to predictor variables (events per variable) was sufficiently small to call into question the accuracy of the regression model. Additionally, no studies reported validation analysis, regression diagnostics, or goodness-of-fit measures. It is recommended that authors, reviewers, and editors pay greater attention to guidelines concerning the use and reporting of LR models.
ISSN:0895-4356
1878-5921
DOI:10.1016/S0895-4356(01)00372-9