AUTHOR=Yan Lihan , Sun Yongmin , Boivin Michael R. , Kwon Paul O. , Li Yuanzhang 

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

JOURNAL=Frontiers in Public Health

VOLUME=4

YEAR=2016

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2016.00207

DOI=10.3389/fpubh.2016.00207

ISSN=2296-2565

ABSTRACT=<p>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.</p>