ArticleJournal of the American Statistical Association2020
Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning.
Article in Journal of the American Statistical Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed.
- Mechanism of an integrated PBL-KAP-AI spiral health education model for breast cancer patients.Frontiers in public health · 2025Trial
- Optimal Dynamic Treatment Regimes for High-Dimensional Accelerated Failure Time Model.Statistics in medicine · 2026Article
- Individual causal effect estimation accounting for latent disease state modification among bipolar participants in mobile health studies.Biometrics · 2026Article
- Evaluating and Optimizing Just-in-Time Adaptive Interventions in a Digital Mental Health Intervention (Wysa for Chronic Pain) for Middle-Aged and Older Adults With Chronic Pain: Protocol for a Series of Randomized Trials.JMIR research protocols · 2025Article
- REINFORCEMENT LEARNING FOR INDIVIDUAL OPTIMAL POLICY FROM HETEROGENEOUS DATA.Annals of statistics · 2025Article
- A multiagent reinforcement learning algorithm for personalized recommendations in bipolar disorder.PNAS nexus · 2025Article
- Reinforcement Learning and Its Clinical Applications Within Healthcare: A Systematic Review of Precision Medicine and Dynamic Treatment Regimes.Healthcare (Basel, Switzerland) · 2025Review
- MULTIVARIATE DYNAMIC MEDIATION ANALYSIS UNDER A REINFORCEMENT LEARNING FRAMEWORK.Annals of statistics · 2025Article
- Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection.Journal of machine learning research : JMLR · 2025Article
- Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Machine Learning and Health Science Research: Tutorial.Journal of medical Internet research · 2024Article
- Federated Offline Reinforcement Learning.Journal of the American Statistical Association · 2024Article
- ASSESSING TIME-VARYING CAUSAL EFFECT MODERATION IN THE PRESENCE OF CLUSTER-LEVEL TREATMENT EFFECT HETEROGENEITY AND INTERFERENCE.Biometrika · 2023Article
- Relative sparsity for medical decision problems.Statistics in medicine · 2023Article
- Monte Carlo sensitivity analysis for unmeasured confounding in dynamic treatment regimes.Biometrical journal. Biometrische Zeitschrift · 2023Observational
- BATCH POLICY LEARNING IN AVERAGE REWARD MARKOV DECISION PROCESSES.Annals of statistics · 2022Article
- The Future of Causal Inference.American journal of epidemiology · 2022Article
- Deep reinforcement learning for personalized treatment recommendation.Statistics in medicine · 2022Article
- GENERALIZATION ERROR BOUNDS OF DYNAMIC TREATMENT REGIMES IN PENALIZED REGRESSION-BASED LEARNING.Annals of statistics · 2022Article
- Limiting adverse birth outcomes in resource-limited settings (LABOR): protocol of a prospective intrapartum cohort study.Gates open research · 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
6 authors.
Funding
Abstract
The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best possible health-care for each patient. Mobile technologies have an important role to play in this vision as they offer a means to monitor a patient's health status in real-time and subsequently to deliver interventions if, when, and in the dose that they are needed. Dynamic treatment regimes formalize individualized treatment plans as sequences of decision rules, one per stage of clinical intervention, that map current patient information to a recommended treatment. However, most existing methods for estimating optimal dynamic treatment regimes are designed for a small number of fixed decision points occurring on a coarse time-scale. We propose a new reinforcement learning method for estimating an optimal treatment regime that is applicable to data collected using mobile technologies in an out-patient setting. The proposed method accommodates an indefinite time horizon and minute-by-minute decision making that are common in mobile health applications. We show that the proposed estimators are consistent and asymptotically normal under mild conditions. The proposed methods are applied to estimate an optimal dynamic treatment regime for controlling blood glucose levels in patients with type 1 diabetes.
Indexed as
Identifiers
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.