A SMART Data Analysis Method for Constructing Adaptive Treatment Strategies for Substance Use Disorders

Overview

Journal Addiction

Specialty Psychiatry

Date 2016 Dec 29

PMID 28029718

Citations 22

Authors

Inbal Nahum-Shani

Ashkan Ertefaie

Xi Lucy Lu

Kevin G Lynch

James R McKay

David W Oslin

Daniel Almirall

Affiliations

Soon will be listed here.

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.

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