Search | Search by Center | Search by Source | Keywords in Title
Nahum-Shani I, Ertefaie A, Lu XL, Lynch KG, McKay JR, Oslin DW, Almirall D. A SMART data analysis method for constructing adaptive treatment strategies for substance use disorders. Addiction (Abingdon, England). 2017 May 1; 112(5):901-909.
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.