Statistical Analysis of Correlated Relative Risks

Rickey E, Carter ； Xu-Yang Zhang ； Robert F. Woolson ； Christian C. Apfel

《Journal of Data Science》 7卷3期 (2009/07) Pp. 397-407

https://doi.org/10.6339/JDS.2009.07(3).458

並列摘要

Much of the statistical literature regarding categorical data focuses on the odds ratio, yet in many epidemiological and clinical trial settings, the relative risk is the quantity of interest. Recently, Spiegelman and Hertzmark illustrated modeling and SAS programming for modeling relative risk in contrast to the logistic model's odds ratio. The focus of their work is on a single relative risk, i.e., for one binary response variable. Herein, we outline two methods for estimating relative risks for two correlated binary outcomes. The first method is weighted least squares estimation for categorical data modeling. The second method is based on generalized estimating equations. The two methods are readily implemented using common statistical packages, such as SAS. The methods are illustrated using clinical trial data examining the relative risks of nausea and vomiting for two different drugs commonly used to provide general anesthesia.

並列關鍵字

Correlated binary data ； generalized estimating equations ； odds ratio ； postoperative nausea and vomiting ； relative risk ； weighted least squares.