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 estimation reliability and accuracy compared with the alternative models. The Joint-DP model is applied to analyze an observational dataset from esophageal cancer patients treated with chemoradiation, including the times of recurrent effusions of fluid to the heart or lungs, survival time, prognostic covariates, and radiation therapy modality.
Original language | English (US) |
---|---|
Pages (from-to) | 221-247 |
Number of pages | 27 |
Journal | Annals of Applied Statistics |
Volume | 13 |
Issue number | 1 |
DOIs | |
State | Published - 2019 |
Keywords
- Accelerated failure time
- Bayesian nonparametrics
- Chemoradiation
- Dirichlet process
- Esophageal cancer
- Joint model
- Nonhomogeneous point process
ASJC Scopus subject areas
- Statistics and Probability
- Modeling and Simulation
- Statistics, Probability and Uncertainty
MD Anderson CCSG core facilities
- Biostatistics Resource Group