ArticleBiostatistics (Oxford, England)2020
Power analysis in a SMART design: sample size estimation for determining the best embedded dynamic treatment regime.
Article in Biostatistics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of Sequential Multiple Assignment Randomized Trials (SMARTs) in oncology: systematic review of published studies.British journal of cancer · 2023Pooled it
- Optimal allocation to treatments in a sequential multiple assignment randomized trial.Statistical methods in medical research · 2021Trial
- Article
- 50k4Life: A SMART study protocol to improve walking engagement in public school employees on the US-Mexico border.Contemporary clinical trials · 2026Article
- Within-trial data borrowing for sequential multiple assignment randomized trials.Biostatistics (Oxford, England) · 2025Article
- A MARGINAL STRUCTURAL MODEL FOR PARTIAL COMPLIANCE IN SMARTS.The annals of applied statistics · 2024Article
- Understanding Physician's Perspectives on AI in Health Care: Protocol for a Sequential Multiple Assignment Randomized Vignette Study.JMIR research protocols · 2024Article
- Article
- 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
- Bayesian set of best dynamic treatment regimes: Construction and sample size calculation for SMARTs with binary outcomes.Statistics in medicine · 2022Article
- MCMTC: A Pragmatic Framework for Selecting an Experimental Design to Inform the Development of Digital Interventions.Frontiers in digital health · 2022Article
- Coulombe et al. Respond to "Baby Steps to a Learning Mental Health-Care System".American journal of epidemiology · 2021Article
- SMARTp: A SMART design for nonsurgical treatments of chronic periodontitis with spatially referenced and nonrandomly missing skewed outcomes.Biometrical journal. Biometrische Zeitschrift · 2020Article
- SMART Binary: New Sample Size Planning Resources for SMART Studies with Binary Outcome Measurements.Multivariate behavioral researchArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Sequential, multiple assignment, randomized trial (SMART) designs have become increasingly popular in the field of precision medicine by providing a means for comparing more than two sequences of treatments tailored to the individual patient, i.e., dynamic treatment regime (DTR). The construction of evidence-based DTRs promises a replacement to ad hoc one-size-fits-all decisions pervasive in patient care. However, there are substantial statistical challenges in sizing SMART designs due to the correlation structure between the DTRs embedded in the design (EDTR). Since a primary goal of SMARTs is the construction of an optimal EDTR, investigators are interested in sizing SMARTs based on the ability to screen out EDTRs inferior to the optimal EDTR by a given amount which cannot be done using existing methods. In this article, we fill this gap by developing a rigorous power analysis framework that leverages the multiple comparisons with the best methodology. Our method employs Monte Carlo simulation to compute the number of individuals to enroll in an arbitrary SMART. We evaluate our method through extensive simulation studies. We illustrate our method by retrospectively computing the power in the Extending Treatment Effectiveness of Naltrexone (EXTEND) trial. An R package implementing our methodology is available to download from the Comprehensive R Archive Network.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.