Modeling longitudinal drinking data in clinical trials: an application to the COMBINE study.

Abstract

There is a lack of consensus in the literature as to how to define drinking outcomes in clinical trials. Typically, separate statistical models are fit to assess treatment effects on several summary drinking measures. These summary measures do not capture the complexity of drinking behavior. We used the COMBINE study to illustrate a statistical approach for examining treatment effects on high-resolution drinking data. This is a secondary data analysis of COMBINE participants randomly assigned to naltrexone, acamprosate, with medical management and/or combined behavioral intervention (CBI). Using a Poisson hurdle model, abstinence and number of drinks were simultaneously modeled as a function of treatment and covariates. An emphasis was placed on the evaluation of "risky drinking" (3 drinks/day for women and 4 for men). During treatment, naltrexone increased the odds of abstinence vs placebo naltrexone (OR=1.35 [1.06, 1.65]) but receiving CBI in addition to naltrexone (vs not) obscured this effect; thus, the naltrexone effect was largest in the group not receiving CBI (OR=1.87 [1.29, 2.46]). Naltrexone vs placebo naltrexone also reduced the risk of drinking in those who resumed risky drinking (RR=0.58 [0.24, 0.93]) and increased the odds of maintaining low risk drinking (OT=1.99 [1.07, 2.90]). Both effects were strongest in the absence of CBI when only "medical management" was provided. The hurdle model is an appropriate statistical tool for assessing the effect of treatment on the two part drinking process, abstinence and number of drinks. When applied to COMBINE, results bolster the use of naltrexone in promoting abstinence and reduction in risky drinking.