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arXiv · 2604.23006

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

Madeline R Abbott·Jeremy M G Taylor·Inbal Nahum-Shani·Lindsey N Potter·David W Wetter·Cho Y Lam·Walter Dempsey
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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.

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Madeline R Abbott, Jeremy M G Taylor, Inbal Nahum-Shani, Lindsey N Potter, David W Wetter, Cho Y Lam, Walter Dempsey. 2026-04-24. Estimation of Time-Varying Treatment Effects in a Joint Model for Longitudinal and Recurrent Event Outcomes in Mobile Health Data. https://arxiv.org/abs/2604.23006

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