Bayesian Semiparametric Joint Regression Analysis of Recurrent Adverse Events and Survival in Esophageal Cancer Patients.

ABSTRACT: We propose a Bayesian semiparametric joint regression model for a recurrent event process and survival time. Assuming independent latent subject frailties, we define marginal models for the recurrent event process intensity and survival distribution as functions of the subject's frailty and baseline covariates. A robust Bayesian model, called Joint-DP, is obtained by assuming a Dirichlet process for the frailty distribution. We present a simulation study that compares posterior estimates under the Joint-DP model to a Bayesian joint model with lognormal frailties, a frequentist joint model, and marginal models for either the recurrent event process or survival time. The simulations show that the Joint-DP model does a good job of correcting for treatment assignment bias, and has favorable es

SUBMITTER: Lee J 

PROVIDER: S-EPMC6824476 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature