The Annals of Applied Statistics

Bayesian semiparametric joint regression analysis of recurrent adverse events and survival in esophageal cancer patients

Juhee Lee, Peter F. Thall, and Steven H. Lin

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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 chemo-radiation, including the times of recurrent effusions of fluid to the heart or lungs, survival time, prognostic covariates, and radiation therapy modality.

Article information

Source
Ann. Appl. Stat., Volume 13, Number 1 (2019), 221-247.

Dates
Received: May 2017
Revised: March 2018
First available in Project Euclid: 10 April 2019

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1554861647

Digital Object Identifier
doi:10.1214/18-AOAS1182

Mathematical Reviews number (MathSciNet)
MR3937427

Zentralblatt MATH identifier
07057426

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

Citation

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


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Supplemental materials

  • 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.