A SMART data analysis method for constructing adaptive treatment strategies for substance use disorders. (18th February 2017)

• Record Type: Journal Article
• Authors: Nahum‐Shani, Inbal; Ertefaie, Ashkan; Lu, Xi (Lucy); Lynch, Kevin G.; McKay, James R.; Oslin, David W.; Almirall, Daniel
• Is Part Of: Addiction. Volume 112:Number 5(2017)
• Page Start: 901
• Page End: 909
• Publication Date: 2017-02-18
• Subjects: Adaptive interventions -- adaptive treatment strategies -- alcohol dependence -- Q‐learning -- Sequential Multiple Assignment Randomized Trial (SMART) -- stepped‐care; Alcoholism -- Periodicals; Drug addiction -- Periodicals; 616.86
• DOI: 10.1111/add.13743
• Languages: English
• ISSNs: 0965-2140

Abstract

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.