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

Frontiers in Public Health

Lihan Yan¹ 

关键词: regression;    logistic;    log-linear;    hazard ratio;    odds ratio;    relative risk;    epidemiology;    principal component analysis;   

DOI  :  10.3389/fpubh.2016.00207

学科分类：卫生学

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY