BAYESIAN SEMIPARAMETRIC JOINT REGRESSION ANALYSIS OF RECURRENT ADVERSE EVENTS AND SURVIVAL IN ESOPHAGEAL CANCER PATIENTS

• 成果类型: Article
• 署名作者: Lee, Juhee; Thall, Peter F.; Lin, Steven H.
• 署名单位: University of California System; University of California Santa Cruz; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/18-AOAS1182
• 发表日期: 2019
• 页码: 221-247
• 关键词: semicompeting risks data; dependent termination; Causal Inference; poisson-process; MODEL; outcomes; COUNT

摘要

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.