Bayesian Joint Longitudinal-Survival Modeling with Time-Dependent Random Effects and Discontinuous Risk Intervals

Abstract

Chronic pain is a common and persistent concern among cancer survivors, particularly among women with a current or prior incidence of breast cancer, and may influence patterns of opioid use and related adverse outcomes. Despite this clinical reality, existing approaches to modeling opioid-related risk typically rely only on baseline covariates and fail to account for the dynamic evolution of pain over time. We develop a Bayesian joint longitudinal–survival modeling framework to assess the relationship between chronic pain trajectories and time to opioid escalation using electronic health record data from a cohort of breast cancer survivors. The model links a longitudinal pain process with a time-to-event outcome through subject-specific random effects, treating pain as an internal time-varying covariate. We further incorporate clinically informed intervention windows (e.g., postoperative periods) during which subjects are not considered at risk for escalation. Posterior inference is performed via a Gibbs-within-Metropolis–Hastings Markov Chain Monte Carlo algorithm. Application of the proposed model to the study cohort data suggests that higher underlying pain burden is associated with increased risk of opioid escalation, while covariate effects reveal heterogeneity in risk across patient subgroups. These results demonstrate the utility of joint modeling approaches and underscore the importance of incorporating longitudinal pain dynamics into event risk modeling in this setting.