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A SMART data analysis method for constructing adaptive treatment strategies for substance use disorders
Author(s) -
NahumShani Inbal,
Ertefaie Ashkan,
Lu Xi Lucy,
Lynch Kevin G.,
McKay James R.,
Oslin David W.,
Almirall Daniel
Publication year - 2017
Publication title -
addiction
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.424
H-Index - 193
eISSN - 1360-0443
pISSN - 0965-2140
DOI - 10.1111/add.13743
Subject(s) - substance use , psychology , computer science , medicine , psychiatry
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.