ArticleJournal of applied statistics2018
Design and Analysis Considerations for Comparing Dynamic Treatment Regimens with Binary Outcomes from Sequential Multiple Assignment Randomized Trials.
Article in Journal of applied statistics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01751698 (Adaptive Interventions for Minimally Verbal Children With ASD in the Community), which is not on this map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Adaptive Interventions for Minimally Verbal Children With ASD in the Community
Who cites it
20 citing papers in PubMed, 40 citations in OpenAlex.
- Scaling and sustaining COVID-19 vaccination through meaningful community engagement and care coordination for underserved communities: hybrid type 3 effectiveness-implementation sequential multiple assignment randomized trial.Implementation science : IS · 2023Trial
- Trial
- Low-touch, team-based care for co-morbidity management in cancer patients: the ONE TEAM randomized controlled trial.BMC family practice · 2021Trial
- Optimal allocation to treatments in a sequential multiple assignment randomized trial.Statistical methods in medical research · 2021Trial
- 50k4Life: A SMART study protocol to improve walking engagement in public school employees on the US-Mexico border.Contemporary clinical trials · 2026Article
- Article
- Within-trial data borrowing for sequential multiple assignment randomized trials.Biostatistics (Oxford, England) · 2025Article
- Addressing sequential and concurrent treatment regimens in a small n sequential, multiple assignment, randomized trial (snSMART) in the MISTIC study.Journal of biopharmaceutical statistics · 2025Article
- Intervention Optimization: A Paradigm Shift and Its Potential Implications for Clinical Psychology.Annual review of clinical psychology · 2024Review
- 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
- A sequential, multiple assignment randomized trial comparing web-based education to mobile video interpreter access for improving provider interpreter use in primary care clinics: the mVOCAL hybrid type 3 study protocol.Implementation science : IS · 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
- Toward a causal model of chronic back pain: Challenges and opportunities.Frontiers in computational neuroscience · 2022Review
- Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness.Statistics in medicine · 2020Article
- Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome.Statistical methods in medical research · 2020Review
- Power analysis in a SMART design: sample size estimation for determining the best embedded dynamic treatment regime.Biostatistics (Oxford, England) · 2020Article
- Dynamic treatment regimens in small n, sequential, multiple assignment, randomized trials: An application in focal segmental glomerulosclerosis.Contemporary clinical trials · 2020Article
- SMART Binary: New Sample Size Planning Resources for SMART Studies with Binary Outcome Measurements.Multivariate behavioral researchArticle
- A Data Analysis Method for Using Longitudinal Binary Outcome Data from a SMART to Compare Adaptive Interventions.Multivariate behavioral researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
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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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.