Inconsistency between univariate and multiple logistic regressions
Author(s): Hongyue WANG, Jing PENG, Bokai WANG, Xiang LU, Julia Z. ZHENG, Kejia WANG, Xin M. TU, Changyong FENG
Pages: 124-128
Year: 2017 Issue: 2
Journal: Shanghai Archives of Psychiatry
Keyword: Conditional expectation; model selection; logistic regression;
Abstract: Summary: 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.
Pages: 124-128
Year: 2017 Issue: 2
Journal: Shanghai Archives of Psychiatry
Keyword: Conditional expectation; model selection; logistic regression;
Abstract: Summary: 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.
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