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Inconsistency Between Univariate and Multiple Logistic Regressions

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 conflictin...

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Detalles Bibliográficos
Autores principales: WANG, Hongyue, PENG, Jing, WANG, Bokai, LU, Xiang, ZHENG, Julia Z., WANG, Kejia, TU, Xin M., FENG, Changyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Shanghai Municipal Bureau of Publishing 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5518262/
https://www.ncbi.nlm.nih.gov/pubmed/28765686
http://dx.doi.org/10.11919/j.issn.1002-0829.217031
Descripción
Sumario: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.