A Weibull Regression Model with Gamma Frailties for Multivariate Survival Data |
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Authors: | Sahu Sujit K Dey Dipak K Aslanidou Helen Sinha Debajyoti |
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Institution: | (1) Statistical Laboratory, University of Cambridge, UK;(2) Department of Statistics, University of Connecticut, Storrs, USA;(3) Department of Mathematics, University of New, Hampshire, USA |
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Abstract: | 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.
This revised version was published online in July 2006 with corrections to the Cover Date. |
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Keywords: | Autocorrelated prior process conditional predictive ordinate frailty Markov chain Monte Carlo methods model determination posterior predictive loss proportional hazards model Weibull model |
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