ArticleBiometrika2021
Estimating time-varying causal excursion effect in mobile health with binary outcomes.
Article in Biometrika, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 53 citations in OpenAlex.
- Pilot microrandomized trial of a brief digital intervention for suicidal thoughts.Journal of consulting and clinical psychology · 2025Trial
- How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized Trial.JMIR mHealth and uHealth · 2023Trial
- Self-relevant appeals to engage in self-monitoring of alcohol use: A microrandomized trial.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2023Trial
- Optimizing Engagement with Digital Mental Health Resources Among Sexual and Gender Minority Users: Protocol for a Series of Microrandomized Trials.JMIR research protocols · 2026Article
- Nonparametric Causal Inference for Optogenetics: Sequential Excursion Effects for Dynamic Regimes.Journal of the American Statistical Association · 2026Article
- Article
- WMRE2030: integrating wearable devices, multi-omics, and artificial intelligence-driven real-time feedback into a daily-scale closed-loop framework for a new era of precision exercise.Frontiers in physiology · 2026Review
- COUNTERFACTUAL INFERENCE IN SEQUENTIAL EXPERIMENTS.Annals of statistics · 2025Article
- Feasibility of mHealth Strategies to Promote Use of Wrist-Worn Alcohol Biosensors Among Young Adults With HIV: Protocol for the Engage Microrandomized Trial.JMIR research protocols · 2025Article
- Replicable Bandits for Digital Health Interventions.Statistical science : a review journal of the Institute of Mathematical Statistics · 2025Article
- Incorporating Auxiliary Variables to Improve the Efficiency of Time-Varying Treatment Effect Estimation.Journal of the American Statistical Association · 2025Article
- Data integration methods for micro-randomized trials.Biometrics · 2025Article
- Designing digital health interventions with causal inference and multi-armed bandits: a review.Frontiers in digital health · 2025Review
- Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling.Machine learning · 2024Article
- mHealth-Based Just-in-Time Adaptive Intervention to Improve the Physical Activity Levels of Individuals With Spinal Cord Injury: Protocol for a Randomized Controlled Trial.JMIR research protocols · 2024Article
- Optimizing an adaptive digital oral health intervention for promoting oral self-care behaviors: Micro-randomized trial protocol.Contemporary clinical trials · 2024Article
- ASSESSING TIME-VARYING CAUSAL EFFECT MODERATION IN THE PRESENCE OF CLUSTER-LEVEL TREATMENT EFFECT HETEROGENEITY AND INTERFERENCE.Biometrika · 2023Article
- Sample size considerations for micro-randomized trials with binary proximal outcomes.Statistics in medicine · 2023Article
- Microrandomized Trials: Developing Just-in-Time Adaptive Interventions for Better Public Health.American journal of public health · 2023Article
- MCMTC: A Pragmatic Framework for Selecting an Experimental Design to Inform the Development of Digital Interventions.Frontiers in digital health · 2022Article
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 4 institutions in 1 country.
Funding
Abstract
Advances in wearables and digital technology now make it possible to deliver behavioral mobile health interventions to individuals in their everyday life. The micro-randomized trial is increasingly used to provide data to inform the construction of these interventions. In a micro-randomized trial, each individual is repeatedly randomized among multiple intervention options, often hundreds or even thousands of times, over the course of the trial. This work is motivated by multiple micro-randomized trials that have been conducted or are currently in the field, in which the primary outcome is a longitudinal binary outcome. The primary aim of such micro-randomized trials is to examine whether a particular time-varying intervention has an effect on the longitudinal binary outcome, often marginally over all but a small subset of the individual's data. We propose the definition of causal excursion effect that can be used in such primary aim analysis for micro-randomized trials with binary outcomes. Under rather restrictive assumptions one can, based on existing literature, derive a semiparametric, locally efficient estimator of the causal effect. Starting from this estimator, we develop an estimator that can be used as the basis of a primary aim analysis under more plausible assumptions. Simulation studies are conducted to compare the estimators. We illustrate the developed methods using data from the micro-randomized trial, BariFit. In BariFit, the goal is to support weight maintenance for individuals who received bariatric surgery.
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What OpenQuestion holds
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.