Aggregating and Analyzing Daily Drinking Data in Clinical Trials: A Comparison of Type I Errors, Power, and Bias

Objective: Statistical analyses in alcohol clinical trials often use longitudinal daily drinking data (e.g., percentage of drinking days) to test treatment efficacy.Such data can be aggregated and analyzed in many ways.To assess how statistical analytic decisions may influence substantive results, the current report compares different aggregation methods (e.g., computing percentages of drinking days vs. using daily binary indicators of drinking) and statistical methods (i.e., least squares regression, linear mixed-effects models [LMM], generalized linear mixed models [GLMM], and generalized estimating equations [GEE]) for testing the effects of treatment on drinking outcomes in clinical trials.Method: A simulation study repeatedly resampled daily drinking data from the treatment period of the Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) Study at different sample sizes.Treatment effects in each data set were modeled using different aggregation and statistical methods.Results: