UCLA

Varenicline has shown promise for treating alcohol use disorder (AUD); however, not everyone will respond to varenicline. Machine-learning methods are well suited to identify treatment responders. In the present study, we examined data from the National Institute on Alcohol Abuse and Alcoholism Clinical Intervention Group multisite clinical trial of varenicline using two machine-learning methods. Baseline characteristics taken from a randomized clinical trial of varenicline were examined as potential moderators of treatment response using qualitative interaction trees (N = 199) and group least absolute shrinkage and selection operator interaction nets (N = 200). Results align with prior research, highlighting smoking status, AUD severity, medication adherence, and drinking goal as predictors of treatment response. Novel findings included the interaction between age and cardiovascular health in predicting clinical response and stronger medication effects among individuals with lower craving. With increased integration of machine-learning methods, studies that effectively integrate methods and medication development have high potential to inform clinical practice.