title: Revealing Facts and Avoiding Biases: A Review of Several Common Problems in Statistical Analyses of Epidemiological Data.
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.
authors: Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration , Silver Spring, MD , USA.
authors: Lihan
authors: L
authors: Yan
authors: Department of Sociology, The Ohio State University , Columbus, OH , USA.
authors: Yongmin
authors: Y
authors: Sun
authors: Preventive Medicine Branch, Walter Reed Army Institute of Research , Silver Spring, MD , USA.
authors: Michael R
authors: MR
authors: Boivin
authors: Preventive Medicine Branch, Walter Reed Army Institute of Research , Silver Spring, MD , USA.
authors: Paul O
authors: PO
authors: Kwon
authors: Preventive Medicine Branch, Walter Reed Army Institute of Research , Silver Spring, MD , USA.
authors: Yuanzhang
authors: Y
authors: Li
keywords: epidemiology; hazard ratio; log-linear; logistic; odds ratio; principal component analysis; regression; relative risk