ReviewStatistical methods in medical research2020
Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome.
Review in Statistical methods in medical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A sequential multiple assignment randomized trial of symptom management for cancer survivors during treatment and their informal caregivers.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024Trial
- Optimal allocation to treatments in a sequential multiple assignment randomized trial.Statistical methods in medical research · 2021Trial
- Censoring-adjusted tree-based policy learning for estimating dynamic treatment regimes with censored outcomes.Scientific reports · 2026Article
- Protocol for the sequential multiple assignment randomized trial to reduce food insecurity and improve adherence in patients with hypertension (SMART-FI).Contemporary clinical trials · 2026Article
- 50k4Life: A SMART study protocol to improve walking engagement in public school employees on the US-Mexico border.Contemporary clinical trials · 2026Article
- Just-in-Time Adaptive Interventions: Where Are We Now and What Is Next?Annual review of psychology · 2026Review
- Feasibility of an ADAPTive intervention to improve food security and Maternal-Child Health (ADAPT-MCH): Protocol for a pilot sequential multiple assignment randomized trial.Contemporary clinical trials · 2025Article
- Approaches to Statistical Efficiency When Comparing the Embedded Adaptive Interventions in a SMART.Journal of educational and behavioral statistics : a quarterly publication sponsored by the American Educational Research Association and the American Statistical Association · 2025Article
- Simulating A/B testing versus SMART designs for LLM-driven patient engagement to close preventive care gaps.NPJ digital medicine · 2024Article
- Sample size estimation for comparing dynamic treatment regimens in a SMART: A Monte Carlo-based approach and case study with longitudinal overdispersed count outcomes.Statistical methods in medical research · 2023Article
- Using Pilot Data for Power Analysis of Observational Studies for the Estimation of Dynamic Treatment Regimes.Observational studies · 2023Article
- The Sequential Multiple Assignment Randomized Trial for Controlling Infectious Diseases: A Review of Recent Developments.American journal of public health · 2023Review
- SMART Binary: New Sample Size Planning Resources for SMART Studies with Binary Outcome Measurements.Multivariate behavioral researchArticle
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Authors and funding
6 authors.
Funding
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.
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