Statistical Analysis of Correlated Relative Risks

Abstract

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 Hertz mark 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 categor ical data modeling. The second method is based on generalized estimating equations. The two methods are readily implemented using common statis tical 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.