ArticleStatistics in medicine2023
Sample size considerations for micro-randomized trials with binary proximal outcomes.
Article in Statistics in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07746492 (A Digital Real-Time Support for Stress-Induced Smoking and Unhealthy Eating Among Gender and Sexual Minorities in Dhaka), which is not on this map. Cited by 6 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.
A Digital Real-Time Support for Stress-Induced Smoking and Unhealthy Eating Among Gender and Sexual Minorities in Dhaka
Who cites it
6 citing papers in PubMed.
- Reminder Strategies to Improve Meal-Logging Adherence: Protocol for a Microrandomized Trial.JMIR research protocols · 2026Article
- Optimizing Engagement with Digital Mental Health Resources Among Sexual and Gender Minority Users: Protocol for a Series of Microrandomized Trials.JMIR research protocols · 2026Article
- Just-in-Time Adaptive Interventions: Where Are We Now and What Is Next?Annual review of psychology · 2026Review
- Article
- The recent history and near future of digital health in the field of behavioral medicine: an update on progress from 2019 to 2024.Journal of behavioral medicine · 2025Review
- Digital Adaptive Behavioral Interventions to Improve HIV Prevention and Care: Innovations in Intervention Approach and Experimental Design.Current HIV/AIDS reports · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Micro-randomized trials (MRTs) are a novel experimental design for developing mobile health interventions. Participants are repeatedly randomized in an MRT, resulting in longitudinal data with time-varying treatments. Causal excursion effects are the main quantities of interest in MRT primary and secondary analyses. We consider MRTs where the proximal outcome is binary and the randomization probability is constant or time-varying but not data-dependent. We develop a sample size formula for detecting a nonzero marginal excursion effect. We prove that the formula guarantees power under a set of working assumptions. We demonstrate via simulation that violations of certain working assumptions do not affect the power, and for those that do, we point out the direction in which the power changes. We then propose practical guidelines for using the sample size formula. As an illustration, the formula is used to size an MRT on interventions for excessive drinking. The sample size calculator is implemented in R package MRTSampleSizeBinary and an interactive R Shiny app. This work can be used in trial planning for a wide range of MRTs with binary proximal outcomes.
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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.