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 discu...

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Bibliographic Details
Published in:Computational statistics & data analysis 2007-07, Vol.51 (11), p.5317-5326
Main Authors: Kheiri, Soleiman, Kimber, Alan, Reza Meshkani, Mohammad
Format: Article
Language:English
Subjects:
Continuous mixture distribution
Exact sciences and technology
Frailty
General topics
Inverse Gaussian distribution
Markov chain Monte Carlo
Mathematics
Multivariate analysis
Nonparametric inference
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Probability and statistics
Proportional hazards
Sciences and techniques of general use
Statistics
Survival analysis
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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.
ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2006.09.026