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Semiparametric Bayesian analysis of accelerated failure time models with cluster structures
Author(s) -
Li Zhaonan,
Xu Xinyi,
Shen Junshan
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7406
Subject(s) - pooling , bayesian probability , computer science , accelerated failure time model , dirichlet distribution , mixture model , nonparametric statistics , statistics , cluster (spacecraft) , dirichlet process , semiparametric model , flexibility (engineering) , econometrics , survival analysis , mathematics , artificial intelligence , mathematical analysis , programming language , boundary value problem
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.