A SMART data analysis method for constructing adaptive treatment strategies for substance use disorders

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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.


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.


Q-learning helped to identify a subset of individuals who, despite showing early signs of response to naltrexone, require additional treatment to maintain progress.


Q-learning can inform the development of more cost-effective, adaptive treatment strategies for treating substance use disorders.

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