JSM 2004 - Toronto

Abstract #300785

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Activity Number: 381
Type: Contributed
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #300785
Title: An Application of Markov Chain Monte Carlo Algorithm to Fit a Bayesian Frailty Model
Author(s): Adriana Lopez*+ and Richard A. Levine and Juanjuan Fan
Companies: San Diego State University and San Diego State University and San Diego State University
Address: 5500 Campanile Dr., San Diego, CA, 92182-0001,
Keywords: Bayesian analysis ; Gibbs sampler ; Metropolis-Hastings sampler ; gamma frailty ; ophthalmology application
Abstract:

Using a frailty model for correlated observations due to a common environment has become an important research topic. We propose a Markov chain Monte Carlo algorithm to fit a Bayesian frailty model having a prior gamma distribution for the frailty parameter. The algorithm is a Metropolis-Hastings sampler, incorporating a Gibbs sampling routine, with priors on the gamma parameters and frailty parameters and an empirical Bayes estimator for the hazard function. The dataset we analyzed considered treatment effect on glaucoma onset. The dataset has two failure times for each patient, the right and the left eye. Each pair of failure times is naturally correlated. The frailty parameter takes account of variables that had not been measured, but could affect the onset of primary open angle glaucoma.

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