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March 2019 Bayesian semiparametric joint regression analysis of recurrent adverse events and survival in esophageal cancer patients
Juhee Lee, Peter F. Thall, Steven H. Lin
Ann. Appl. Stat. 13(1): 221-247 (March 2019). DOI: 10.1214/18-AOAS1182


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.


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Juhee Lee. Peter F. Thall. Steven H. Lin. "Bayesian semiparametric joint regression analysis of recurrent adverse events and survival in esophageal cancer patients." Ann. Appl. Stat. 13 (1) 221 - 247, March 2019.


Received: 1 May 2017; Revised: 1 March 2018; Published: March 2019
First available in Project Euclid: 10 April 2019

zbMATH: 07057426
MathSciNet: MR3937427
Digital Object Identifier: 10.1214/18-AOAS1182

Keywords: Accelerated failure time , Bayesian nonparametrics , chemoradiation , Dirichlet process , esophageal cancer , joint model , nonhomogeneous point process

Rights: Copyright © 2019 Institute of Mathematical Statistics


Vol.13 • No. 1 • March 2019
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