A Weibull regression model with gamma frailties for multivariate survival data
A Weibull regression model with gamma frailties for multivariate survival data
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.
autocorrelated prior process, conditional predictive ordinate, frailty, markov chain monte carlo methods, model determination, posterior predictive loss, proportional hazards model, weibull model
123-137
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Dey, Dipak K.
d627f0e8-acb8-4b78-8810-0e25259fb6e8
Aslanidou, Helen
d9332305-ef99-4c97-b8db-fc61009f923b
Sinha, Debajyoti
2a56079e-e401-48e0-b54f-b8aa8ad7ef7a
1997
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Dey, Dipak K.
d627f0e8-acb8-4b78-8810-0e25259fb6e8
Aslanidou, Helen
d9332305-ef99-4c97-b8db-fc61009f923b
Sinha, Debajyoti
2a56079e-e401-48e0-b54f-b8aa8ad7ef7a
Sahu, Sujit K., Dey, Dipak K., Aslanidou, Helen and Sinha, Debajyoti
(1997)
A Weibull regression model with gamma frailties for multivariate survival data.
Lifetime Data Analysis, 3 (2), .
(doi:10.1023/A:1009605117713).
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.
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Published date: 1997
Keywords:
autocorrelated prior process, conditional predictive ordinate, frailty, markov chain monte carlo methods, model determination, posterior predictive loss, proportional hazards model, weibull model
Organisations:
Statistics
Identifiers
Local EPrints ID: 30019
URI: http://eprints.soton.ac.uk/id/eprint/30019
ISSN: 1380-7870
PURE UUID: 88e849d6-d68a-4197-bd2c-b4f30601245a
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Date deposited: 11 May 2007
Last modified: 16 Mar 2024 03:15
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Contributors
Author:
Dipak K. Dey
Author:
Helen Aslanidou
Author:
Debajyoti Sinha
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