Survival Regression Models With Dependent Bayesian Nonparametric Priors
成果类型：
Article
署名作者：
Riva-Palacio, Alan; Leisen, Fabrizio; Griffin, Jim
署名单位：
Universidad Nacional Autonoma de Mexico; University of Nottingham; University of London; University College London
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1080/01621459.2020.1864381
发表日期：
2022
页码：
1530-1539
关键词：
摘要：
We present a novel Bayesian nonparametric model for regression in survival analysis. Our model builds on the classical neutral to the right model of Doksum and on the Cox proportional hazards model of Kim and Lee. The use of a vector of dependent Bayesian nonparametric priors allows us to efficiently model the hazard as a function of covariates while allowing nonproportionality. The model can be seen as having competing latent risks. We characterize the posterior of the underlying dependent vector of completely random measures and study the asymptotic behavior of the model. We show how an MCMC scheme can provide Bayesian inference for posterior means and credible intervals. The method is illustrated using simulated and real data. for this article are available online.
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