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

作者：Nahum Shani Inbal; Ertefaie Ashkan; Lu Xi; Lynch Kevin G; McKay James R; Oslin David W; Almirall Daniel

来源：Addiction, 2017, 112(5): 901-909.

DOI：10.1111/add.13743

• Adaptive interventions
• adaptive treatment strategies
• alcohol dependence
• Q-learning
• Sequential Multiple Assignment Randomized Trial (SMART)
• stepped-care

AimsTo 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.
MethodWe 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 24weeks in alcohol dependent individuals.
ResultsQ-learning helped to identify a subset of individuals who, despite showing early signs of response to naltrexone, require additional treatment to maintain progress.
ConclusionsQ-learning can inform the development of more cost-effective, adaptive treatment strategies for treating substance use disorders.

• 出版日期2017-5