Bayesian Model of Gap Times for Multitype Recurrent and a Terminal Event: A Joint Dynamic Framework

Music City Center 

In biomedical research, recurrent events like coronary heart disease, stroke, and heart failure often result in terminal outcomes such as death. Understanding these relationships is essential for developing effective interventions. This study proposes a Bayesian semiparametric joint dynamic model that captures event dependencies, cumulative effects of past recurrent events on themselves and terminal events, covariates, and frailty. Gamma process priors are used for the baseline cumulative hazard function (CHF) and parametric priors for covariates and frailty. In addition to incorporating gap time distributions for more accurate risk assessment, this model provides an analytical closed-form estimator of CHF and parameter estimates through MCMC. Breslow-Aalen-type estimators of baseline CHFs are special cases of our estimators when precision parameters are set to zero. The model's accuracy and superiority are validated through simulations over the frequentist model, while its application to the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT-LLT) study, offers new insights into preventing cardiovascular disease and reducing its mortality risks.

Gap time

Multitype recurrent events

Terminal event

Bayesian semiparametric joint dynamic model

Gamma process prior

ALLHAT-LLT Study 

Biometrics Section