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	Semiparametric Bayesian analysis of accelerated failure time models with cluster structures
	Li, Zhaonan ; Xu, Xinyi ; Shen, Junshan
	2017
关键词	density ratio model mixture of Dirichlet processes survival analysis REGRESSION-MODELS EFFICIENT ESTIMATION DIRICHLET PROCESS CENSORED-DATA LIKELIHOOD INFERENCE MIXTURES QUANTILE PRIORS
英文摘要	In this paper, we develop a Bayesian semiparametric accelerated failure time model for survival data with cluster structures. Our model allows distributional heterogeneity across clusters and accommodates their relationships through a density ratio approach. Moreover, a nonparametric mixture of Dirichlet processes prior is placed on the baseline distribution to yield full distributional flexibility. We illustrate through simulations that our model can greatly improve estimation accuracy by effectively pooling information from multiple clusters, while taking into account the heterogeneity in their random error distributions. We also demonstrate the implementation of our method using analysis of Mayo Clinic Trial in Primary Biliary Cirrhosis.; National Science Foundation [DMS-1613110]; National Natural Science Foundation of China [11471024, 11171230]; SCI(E); ARTICLE; 25; 3976-3989; 36
语种	英语
出处	SCI
出版者	STATISTICS IN MEDICINE
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/470392]
专题	数学科学学院
推荐引用方式 GB/T 7714	Li, Zhaonan,Xu, Xinyi,Shen, Junshan. Semiparametric Bayesian analysis of accelerated failure time models with cluster structures. 2017-01-01.

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