Estimation of Time-Varying Treatment Effects in a Joint Model for Longitudinal and Recurrent Event Outcomes in Mobile Health Data

2026-04-24

AI Summary

This study addresses the challenge of estimating time-varying treatment effects in mobile health micro-randomized trials, where repeated interventions influence both longitudinal outcomes and recurrent events. The authors propose an innovative joint longitudinal-survival modeling framework that, for the first time, incorporates time-varying treatment effects from micro-randomized trials into a unified joint model. By leveraging Bayesian inference, the approach flexibly captures dynamic associations among repeated treatments, multidimensional longitudinal markers, and recurrent event times, while accommodating diverse treatment effect mechanisms. Model selection is guided by information criteria, and the performance of the survival submodel is evaluated using calibration plots. Simulation studies and an analysis of a substance use micro-randomized trial demonstrate that the proposed method achieves excellent model fit and accurate estimation of treatment effects.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Life-Long and Continual Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Not only does mobile health technology enable researchers to track changes in multiple longitudinal outcomes of interest and to record the occurrence of health-related events over time, but it also allows for the delivery of repeated low-cost treatments directly to individuals in real time. We present a model-based approach for estimating the effect of repeatedly delivered treatments in a micro-randomized trial (MRT) via an extension of a joint longitudinal-survival model. We discuss different ways that these repeated treatment effects can be incorporated into the joint model; these different model specifications correspond to different mechanisms by which treatment is assumed to impact the longitudinal and event processes. Taking a Bayesian approach to inference, we model the association between repeated treatments, multiple longitudinally measured outcomes, and recurrent events. We also demonstrate how to calculate information criteria for model selection and present goodness-of-fit plots for assessing survival submodel calibration. We then illustrate the performance of our method via simulations and analysis of data collected in an MRT of substance use.

Problem

• time-varying treatment effects
• joint model
• longitudinal outcomes
• recurrent events
• mobile health

Innovation

• micro-randomized trial
• joint longitudinal-survival model
• time-varying treatment effects
• Bayesian inference
• mobile health