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Revealing Facts and Avoiding Biases: A Review of Several Common Problems in Statistical Analyses of Epidemiological
Lihan Yan1, Yongmin Sun2, Michael R Boivin3
1Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration , Silver Spring, MD , USA.
Abstract:
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.
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