ANALYSIS OF PREVENTION PROGRAM EFFECTIVENESS WITH CLUSTERED DATA USING GENERALIZED ESTIMATING EQUATIONS

Experimental studies of prevention programs often randomize clusters o f individuals rather than individuals to treatment conditions. When th e correlation among individuals within clusters is not accounted for i n statistical analysis, the standard errors are biased, potentially re sulting in misleading conclusions about the significance of treatment effects. This study demonstrates the generalized estimating equations (GEE) method, focusing specifically on the GEE-independent method, to control for within-cluster correlation in regression models with eithe r continuous or binary outcomes. The GEE-independent method yields con sistent and robust variance estimates. Data from Project DARE, a youth substance abuse prevention program, are used for illustration.