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

Addiction. 2017 May;112(5):901-909. doi: 10.1111/add.13743. Epub 2017 Feb 18.

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.

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

MeSH terms

  • Adaptive Clinical Trials as Topic*
  • Alcoholism / therapy*
  • Behavior Therapy / methods*
  • Data Interpretation, Statistical*
  • Disease Management
  • Female
  • Humans
  • Male
  • Naltrexone / therapeutic use*
  • Narcotic Antagonists / therapeutic use*
  • Randomized Controlled Trials as Topic*
  • Regression Analysis
  • Telephone
  • Treatment Outcome

Substances

  • Narcotic Antagonists
  • Naltrexone