| Summary: | This paper reviews 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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