Revealing Facts and Avoiding Biases: A Review of Several Common Problems in Statistical Analyses of Epidemiological Data

This paper reviews several common challenges encountered in statistical analyses of epidemiological data for epidemiologists. We focus on the application of linear regression, multivariate logistic regression, and log-linear modeling to epidemiological data. Specific topics include: (a) deletion of...

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Published inFrontiers in public health Vol. 4; p. 207
Main Authors Yan, Lihan, Sun, Yongmin, Boivin, Michael R, Kwon, Paul O, Li, Yuanzhang
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 07.10.2016
Subjects
Epidemiology
heteroscedasticity
Odds Ratio
Principal Component Analysis
Public Health
regression
review
epidemiology
log-linear
logistic
regression
hazard ratio
odds ratio
principal component analysis
relative risk
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Summary:This paper reviews several common challenges encountered in statistical analyses of epidemiological data for epidemiologists. We focus on the application of linear regression, multivariate logistic regression, and log-linear modeling to epidemiological data. Specific topics include: (a) deletion of outliers, (b) heteroscedasticity in linear regression, (c) limitations of principal component analysis in dimension reduction, (d) hazard ratio vs. odds ratio in a rate comparison analysis, (e) log-linear models with multiple response data, and (f) ordinal logistic vs. multinomial logistic models. As a general rule, a thorough examination of a model's assumptions against both current data and prior research should precede its use in estimating effects.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ObjectType-Review-1
Reviewed by: Hui Hu, University of Florida, USA; Xiangzhu Zhu, Vanderbilt University, USA; Carmen Tekwe, Texas A&M University, USA
Edited by: Xiaohui Xu, Texas A&M University Health Science Center, USA
Specialty section: This article was submitted to Epidemiology, a section of the journal Frontiers in Public Health
ISSN:2296-2565
2296-2565
DOI:10.3389/fpubh.2016.00207