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

ABSTRACT: 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, adaptive treatment strategies for treating substance use disorders.

SUBMITTER: Nahum-Shani I 

PROVIDER: S-EPMC5431579 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature