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Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome.
Seewald, Nicholas J; Kidwell, Kelley M; Nahum-Shani, Inbal; Wu, Tianshuang; McKay, James R; Almirall, Daniel.
Affiliation
  • Seewald NJ; Department of Statistics, University of Michigan, Ann Arbor, MI, USA.
  • Kidwell KM; Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
  • Nahum-Shani I; Institute for Social Research, University of Michigan, Ann Arbor, MI, USA.
  • Wu T; AbbVie, North Chicago, IL, USA.
  • McKay JR; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Almirall D; Department of Statistics, University of Michigan, Ann Arbor, MI, USA.
Stat Methods Med Res ; 29(7): 1891-1912, 2020 07.
Article in En | MEDLINE | ID: mdl-31571526
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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Research Design Type of study: Clinical_trials / Prognostic_studies Limits: Humans Language: En Journal: Stat Methods Med Res Year: 2020 Document type: Article Affiliation country: United States Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Research Design Type of study: Clinical_trials / Prognostic_studies Limits: Humans Language: En Journal: Stat Methods Med Res Year: 2020 Document type: Article Affiliation country: United States Country of publication: United kingdom