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Bayesian analysis of an inverse Gaussian correlated frailty model

Kheiri, S., Kimber, A. and Meshkani, M. R. (2007) Bayesian analysis of an inverse Gaussian correlated frailty model. Computational Statistics & Data Analysis, 51 (11). pp. 5317-5326. ISSN 0167-9473

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To link to this item DOI: 10.1016/j.csda.2006.09.026

Abstract/Summary

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. (C) 2006 Elsevier B.V. All rights reserved.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences
ID Code:10031
Uncontrolled Keywords:continuous mixture distribution, frailty, inverse Gaussian, distribution, Markov chain Monte Carlo, proportional hazards, survival, analysis, SURVIVAL-DATA, MONTE-CARLO, TIMES

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