A Weibull Regression Model with Gamma Frailties for Multivariate Survival Data

被引：99

作者：

Sahu S.K. ^{[1
]}

Dey D.K. ^{[2
]}

Aslanidou H. ^{[2
]}

Sinha D. ^{[3
]}

机构：

[1] Statistical Laboratory, University of Cambridge

[2] Department of Statistics, University of Connecticut, Storrs, CT

[3] Department of Mathematics, University of New Hampshire

来源：

Lifetime Data Analysis | 1997年 / 3卷 / 2期

基金：

英国工程与自然科学研究理事会; 美国国家科学基金会;

关键词：

Autocorrelated prior process; Conditional predictive ordinate; Frailty; Markov chain Monte Carlo methods; Model determination; Posterior predictive loss; Proportional hazards model; Weibull model;

D O I：

10.1023/A:1009605117713

中图分类号：

学科分类号：

摘要：

Frequently in the analysis of survival data, survival times within the same group are correlated due to unobserved co-variates. One way these co-variates can be included in the model is as frailties. These frailty random block effects generate dependency between the survival times of the individuals which are conditionally independent given the frailty. Using a conditional proportional hazards model, in conjunction with the frailty, a whole new family of models is introduced. By considering a gamma frailty model, often the issue is to find an appropriate model for the baseline hazard function. In this paper a flexible baseline hazard model based on a correlated prior process is proposed and is compared with a standard Weibull model. Several model diagnostics methods are developed and model comparison is made using recently developed Bayesian model selection criteria. The above methodologies are applied to the McGilchrist and Aisbett (1991) kidney infection data and the analysis is performed using Markov Chain Monte Carlo methods.

引用

页码：123 / 137

页数：14

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