Bayesian analysis of an inverse Gaussian correlated frailty model
Bayesian analysis of an inverse Gaussian correlated frailty model
In survival analysis frailty is often used to model heterogeneity between individuals or correlation within clusters. Typically frailty is taken to be a continuous random effect, yielding a continuous mixture distribution for survival times. A Bayesian analysis of a correlated frailty model is discussed in the context of inverse Gaussian frailty. An MCMC approach is adopted and the deviance information criterion is used to compare models. As an illustration of the approach a bivariate data set of corneal graft survival times is analysed.
continuous mixture distribution, frailty, inverse gaussian distribution, markov chain, monte carlo, proportional hazards, survival analysis
5317-5326
Kheiri, Soleiman
1d7ac74c-ffbf-4d43-abb6-9515ec76b3ef
Kimber, Alan
40ba3a19-bbe3-47b6-9a8d-68ebf4cea774
Meshkani, Mohammad Reza
270ce77f-dddf-445a-ae3d-12d63fd4764c
2007
Kheiri, Soleiman
1d7ac74c-ffbf-4d43-abb6-9515ec76b3ef
Kimber, Alan
40ba3a19-bbe3-47b6-9a8d-68ebf4cea774
Meshkani, Mohammad Reza
270ce77f-dddf-445a-ae3d-12d63fd4764c
Kheiri, Soleiman, Kimber, Alan and Meshkani, Mohammad Reza
(2007)
Bayesian analysis of an inverse Gaussian correlated frailty model.
Computational Statistics and Data Analysis, 51 (11), .
(doi:10.1016/j.csda.2006.09.026).
Abstract
In survival analysis frailty is often used to model heterogeneity between individuals or correlation within clusters. Typically frailty is taken to be a continuous random effect, yielding a continuous mixture distribution for survival times. A Bayesian analysis of a correlated frailty model is discussed in the context of inverse Gaussian frailty. An MCMC approach is adopted and the deviance information criterion is used to compare models. As an illustration of the approach a bivariate data set of corneal graft survival times is analysed.
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Published date: 2007
Keywords:
continuous mixture distribution, frailty, inverse gaussian distribution, markov chain, monte carlo, proportional hazards, survival analysis
Organisations:
Statistics
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Local EPrints ID: 42004
URI: http://eprints.soton.ac.uk/id/eprint/42004
ISSN: 0167-9473
PURE UUID: 29250774-af18-4137-894c-601926735481
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Date deposited: 30 Oct 2006
Last modified: 15 Mar 2024 08:42
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Author:
Soleiman Kheiri
Author:
Mohammad Reza Meshkani
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