Experimental study designs in which individuals are randomly assigned to two or more treatment arms are common in clinical research and considered the gold standard. A serious problem arises if study participants do not fully adhere to their assigned treatment regimen, because this undermines the accurate evaluation of the risks and benefits of treatment. Imperfect adherence is common in trials studying the efficacy of treatments for alcohol dependence and requires the use of statistical models and methods that can properly incorporate both adherence behavior and health outcomes. Several methods are available, but they often rely on a number of statistical assumptions that are not necessarily realistic or may not be verifiable, yet can strongly influence the estimation results. This study wll develop an alternative and innovative Bayesian method for estimating treatment efficacy in the presence of imperfect treatment adherence and will evaluate it relative to competing approaches. This method is not based on some of the commonly invoked statistical assumptions, and therefore carries a smaller risk of bias in the efficacy estimates. Additionally, researcher has the option to incorporate external information, for example from other trials, into the evaluation, which can lead to more precise inference. The method will be applied in an empirical analysis of data from the COMBINE study, which is one of the largest and best known randomized trials for treatment of alcohol dependence and contains detailed information about individual adherence. In summary, this study has the following two specific aims: (1) to develop an innovative Bayesian method for estimating treatment efficacy in randomized studies with imperfect treatment adherence and to compare its performance relative to competing approaches;and (2) to apply this method to estimate the efficacy of pharmacological and behavioral interventions for alcohol dependence in the COMBINE study. The research team, consisting of experts in Bayesian methods, the COMBINE study and alcohol studies research, and clinical trial design and analysis, is in an excellent position to successfully complete the proposed research. The results from this work will provide researchers with a valuable and practical new tool for evaluating treatment efficacy in the presence of imperfect treatment adherence.
Poor adherence to the assigned treatment regimen is common in randomized studies and presents a serious challenge by undermining the accurate evaluation of the risks and benefits of treatments. The proposed study will develop an innovative statistical method that produces accurate and unbiased estimates of treatment efficacy even when treatment adherence is not perfect. The results of this research have an important public health benefit because they allow a more accurate assessment of the efficacy of new treatments and can help predict the effect of treatment in new patients.