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- Title
Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome.
- Authors
Seewald, Nicholas J; Kidwell, Kelley M; Nahum-Shani, Inbal; Wu, Tianshuang; McKay, James R; Almirall, Daniel
- Abstract
Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen is a sequence of prespecified decision rules which can be used to guide the delivery of a sequence of treatments or interventions that is tailored to the changing needs of the individual. The sequential multiple-assignment randomized trial is a research tool which allows for the construction of effective dynamic treatment regimens. We derive easy-to-use formulae for computing the total sample size for three common two-stage sequential multiple-assignment randomized trial designs in which the primary aim is to compare mean end-of-study outcomes for two embedded dynamic treatment regimens which recommend different first-stage treatments. The formulae are derived in the context of a regression model which leverages information from a longitudinal outcome collected over the entire study. We show that the sample size formula for a sequential multiple-assignment randomized trial can be written as the product of the sample size formula for a standard two-arm randomized trial, a deflation factor that accounts for the increased statistical efficiency resulting from a longitudinal analysis, and an inflation factor that accounts for the design of a sequential multiple-assignment randomized trial. The sequential multiple-assignment randomized trial design inflation factor is typically a function of the anticipated probability of response to first-stage treatment. We review modeling and estimation for dynamic treatment regimen effect analyses using a longitudinal outcome from a sequential multiple-assignment randomized trial, as well as the estimation of standard errors. We also present estimators for the covariance matrix for a variety of common working correlation structures. Methods are motivated using the ENGAGE study, a sequential multiple-assignment randomized trial aimed at developing a dynamic treatment regimen for increasing motivation to attend treatments among alcohol- and cocaine-dependent patients.
- Subjects
TREATMENT effectiveness; COVARIANCE matrices; ALCOHOL; INDIVIDUAL needs; REGRESSION analysis; WORK structure; EXPERIMENTAL design; CLINICAL trials; SAMPLE size (Statistics)
- Publication
Statistical Methods in Medical Research, 2020, Vol 29, Issue 7, p1891
- ISSN
0962-2802
- Publication type
journal article
- DOI
10.1177/0962280219877520