The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 13, Number 1 (2019), 221-247.
Bayesian semiparametric joint regression analysis of recurrent adverse events and survival in esophageal cancer patients
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 chemo-radiation, including the times of recurrent effusions of fluid to the heart or lungs, survival time, prognostic covariates, and radiation therapy modality.
Ann. Appl. Stat., Volume 13, Number 1 (2019), 221-247.
Received: May 2017
Revised: March 2018
First available in Project Euclid: 10 April 2019
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Lee, Juhee; Thall, Peter F.; Lin, Steven H. Bayesian semiparametric joint regression analysis of recurrent adverse events and survival in esophageal cancer patients. Ann. Appl. Stat. 13 (2019), no. 1, 221--247. doi:10.1214/18-AOAS1182. https://projecteuclid.org/euclid.aoas/1554861647
- Supplement to “Bayesian semiparametric joint regression analysis of recurrent adverse events and survival in esophageal cancer patients”. Joint Bayesian semiparametric regression analysis of recurrent adverse events and survival in esophageal cancer patients are available under the paper information link at the Journal website.