Key result
Univariate and multiple logistic regressions can yield conflicting results regarding covariate effects, highlighting the need for careful model selection in biomedical research.
Key points are not available for this paper at this time.
Discordant univariate vs multivariable logistic results risk misinterpretation; leaves open optimal reporting standards for observational cardiovascular analyses.
Logistic regression is a popular statistical method in studying the effects of covariates on binary outcomes. It has been widely used in both clinical trials and observational studies. However, the results from the univariate regression and from the multiple logistic regression tend to be conflicting. A covariate may show very strong effect on the outcome in the multiple regression but not in the univariate regression, and vice versa. These facts have not been well appreciated in biomedical research. Misuse of logistic regression is very prevalent in medical publications. In this paper, we study the inconsistency between the univariate and multiple logistic regressions and give advice in the model section in multiple logistic regression analysis.
No takes yet. Share an insight, caveat, or question.
Wang et al. (2017) studied this question. Univariate and multiple logistic regressions can yield conflicting results regarding covariate effects, highlighting the need for careful model selection in biomedical research.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: