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

Type I error rates were near the expected rate for most models but on occasion were mildly elevated when disaggregated daily drinking data were analyzed using GLMM or GEE with an exchangeable correlation structure. Most methods yielded similar statistical power, although power decreased when modeling disaggregated daily drinking with GLMM and had mixed increases and decreases when the longitudinal nature of data was ignored by using fully aggregated data with independent samples t tests.

CONCLUSIONS

When testing treatment main effects, relatively simpler statistical methods with fewer repeated measures may perform equally well or better than more complicated methods. Patterns of significance and treatment effect size estimates are likely comparable across most studies that use different aggregation and statistical methods, but differences between these methods may occasionally have an important impact on conclusions in clinical trials.