Design and analysis considerations for comparing dynamic treatment regimens with binary outcomes from sequential multiple assignment randomized trials |
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Authors: | Kelley M. Kidwell Nicholas J. Seewald Qui Tran Connie Kasari Daniel Almirall |
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Affiliation: | 1. Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA;2. Department of Statistics, University of Michigan, Ann Arbor, MI, USA;3. Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, MI, USA;4. Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA |
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Abstract: | In behavioral, educational and medical practice, interventions are often personalized over time using strategies that are based on individual behaviors and characteristics and changes in symptoms, severity, or adherence that are a result of one's treatment. Such strategies that more closely mimic real practice, are known as dynamic treatment regimens (DTRs). A sequential multiple assignment randomized trial (SMART) is a multi-stage trial design that can be used to construct effective DTRs. This article reviews a simple to use ‘weighted and replicated’ estimation technique for comparing DTRs embedded in a SMART design using logistic regression for a binary, end-of-study outcome variable. Based on a Wald test that compares two embedded DTRs of interest from the ‘weighted and replicated’ regression model, a sample size calculation is presented with a corresponding user-friendly applet to aid in the process of designing a SMART. The analytic models and sample size calculations are presented for three of the more commonly used two-stage SMART designs. Simulations for the sample size calculation show the empirical power reaches expected levels. A data analysis example with corresponding code is presented in the appendix using data from a SMART developing an effective DTR in autism. |
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Keywords: | Adaptive interventions dynamic treatment regimes sequential multiple assignment randomized trial inverse-probability-of-treatment weighting sample size |
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