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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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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
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author WANG, Hongyue
PENG, Jing
WANG, Bokai
LU, Xiang
ZHENG, Julia Z.
WANG, Kejia
TU, Xin M.
FENG, Changyong
author_facet WANG, Hongyue
PENG, Jing
WANG, Bokai
LU, Xiang
ZHENG, Julia Z.
WANG, Kejia
TU, Xin M.
FENG, Changyong
author_sort WANG, Hongyue
collection PubMed
description 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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spelling pubmed-55182622017-08-01 Inconsistency Between Univariate and Multiple Logistic Regressions WANG, Hongyue PENG, Jing WANG, Bokai LU, Xiang ZHENG, Julia Z. WANG, Kejia TU, Xin M. FENG, Changyong Shanghai Arch Psychiatry Biostatistics in Psychiatry (38) 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. Shanghai Municipal Bureau of Publishing 2017-04-25 2017-04-25 /pmc/articles/PMC5518262/ /pubmed/28765686 http://dx.doi.org/10.11919/j.issn.1002-0829.217031 Text en © Shanghai Municipal Bureau of Publishing http://creativecommons.org/licenses/by-nc/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/
spellingShingle Biostatistics in Psychiatry (38)
WANG, Hongyue
PENG, Jing
WANG, Bokai
LU, Xiang
ZHENG, Julia Z.
WANG, Kejia
TU, Xin M.
FENG, Changyong
Inconsistency Between Univariate and Multiple Logistic Regressions
title Inconsistency Between Univariate and Multiple Logistic Regressions
title_full Inconsistency Between Univariate and Multiple Logistic Regressions
title_fullStr Inconsistency Between Univariate and Multiple Logistic Regressions
title_full_unstemmed Inconsistency Between Univariate and Multiple Logistic Regressions
title_short Inconsistency Between Univariate and Multiple Logistic Regressions
title_sort inconsistency between univariate and multiple logistic regressions
topic Biostatistics in Psychiatry (38)
url 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
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