A Bayesian joint longitudinal-survival model with a latent stochastic process for intensive longitudinal data
arxiv(2024)
摘要
The availability of mobile health (mHealth) technology has enabled increased
collection of intensive longitudinal data (ILD). ILD have potential to capture
rapid fluctuations in outcomes that may be associated with changes in the risk
of an event. However, existing methods for jointly modeling longitudinal and
event-time outcomes are not well-equipped to handle ILD due to the high
computational cost. We propose a joint longitudinal and time-to-event model
suitable for analyzing ILD. In this model, we summarize a multivariate
longitudinal outcome as a smaller number of time-varying latent factors. These
latent factors, which are modeled using an Ornstein-Uhlenbeck stochastic
process, capture the risk of a time-to-event outcome in a parametric hazard
model. We take a Bayesian approach to fit our joint model and conduct
simulations to assess its performance. We use it to analyze data from an
mHealth study of smoking cessation. We summarize the longitudinal self-reported
intensity of nine emotions as the psychological states of positive and negative
affect. These time-varying latent states capture the risk of the first smoking
lapse after attempted quit. Understanding factors associated with smoking lapse
is of keen interest to smoking cessation researchers.
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