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

TL;DR

This paper introduces a Bayesian joint modeling approach to estimate time-varying treatment effects in mobile health studies, integrating longitudinal outcomes and recurrent events, with applications demonstrated through simulations and substance use data.

Contribution

It extends joint longitudinal-survival models to incorporate repeated treatments in micro-randomized trials, providing a flexible framework for effect estimation in mobile health data.

Findings

The proposed model accurately estimates treatment effects in simulations.

Model selection and goodness-of-fit methods are effectively demonstrated.

Application to substance use data illustrates practical utility.

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.