A SMART Data Analysis Method for Constructing Adaptive Treatment Strategies for Substance Use Disorders

Aims To demonstrate how Q-learning, a novel data analysis method, can be used with data from a sequential, multiple assignment, randomized trial (SMART) to construct empirically an adaptive treatment strategy (ATS) that is more tailored than the ATSs already embedded in a SMART. Method We use Q-learning with data from the Extending Treatment Effectiveness of Naltrexone (ExTENd) SMART (N = 250) to construct empirically an ATS employing naltrexone, behavioral intervention, and telephone disease management to reduce alcohol consumption over 24 weeks in alcohol dependent individuals. Results Q-learning helped to identify a subset of individuals who, despite showing early signs of response to naltrexone, require additional treatment to maintain progress. Conclusions Q-learning can inform the development of more cost-effective, stepped-care strategies for treating substance use disorders.