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Activity Number:
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301
- Design and Analysis Tools for Mental Health Research
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Type:
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Contributed
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Date/Time:
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Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
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Sponsor:
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Mental Health Statistics Section
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Abstract #304357
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Presentation
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Title:
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Sample Size Considerations for Comparing Dynamic Treatment Regimens in a SMART with a Repeated-Measures Outcome
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Author(s):
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Nicholas Seewald* and Daniel Almirall
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Companies:
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University of Michigan and University of Michigan
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Keywords:
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Dynamic treatment regimen;
Sequential multiple assignment randomized trial;
Randomized trial;
Longitudinal data;
Sample size
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Abstract:
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Clinicians and researchers are increasingly interested in how best to individualize interventions. A dynamic treatment regimen (DTR) is a sequence of pre-specified decision rules which guide the delivery of a course of treatments that is tailored to the changing needs of the individual. The sequential multiple-assignment randomized trial (SMART) is a research tool that can be used to inform the construction of effective DTRs. We introduce sample size formulae for SMARTs in which the primary aim is to compare two embedded DTRs using a continuous repeated-measures outcome collected at three timepoints throughout the study. The method is based on a longitudinal analysis that accounts for unique features of a SMART, including modeling constraints and the over/under-representation of different sequences of treatment among participants. We also consider an extension to choose both sample size and the number of measurement occasions in a SMART in order to maximize statistical power subject to a budget constraint. We illustrate the method using ENGAGE, a SMART aimed at developing a DTR for re-engaging patients with alcohol and/or cocaine use disorders who have dropped out of treatment.
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Authors who are presenting talks have a * after their name.
