Dataset Information
A SMART data analysis method for constructing adaptive treatment strategies for substance use disorders.
Ontology highlight
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.
SUBMITTER: Nahum-Shani I
PROVIDER: S-EPMC5431579 | biostudies-literature | 2017 May
REPOSITORIES: biostudies-literature
Publications
A SMART data analysis method for constructing adaptive treatment strategies for substance use disorders.
Addiction (Abingdon, England) 20170218 5
<h4>Aims</h4>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.<h4>Method</h4>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 telep ...[more]