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BAYESIAN MODEL AVERAGING FOR HETEROGENEOUS FRAILTY  

Chang, Il-Sung (Johnson and Johnson Pharmaceutical Research and Development)
Lim, Jo-Han (Department of Applied Statistics, Yonsei University)
Publication Information
Journal of the Korean Statistical Society / v.36, no.1, 2007 , pp. 129-148 More about this Journal
Abstract
Frailty estimates from the proportional hazards frailty model often lead us to conjecture the heterogeneity in frailty such that the variance of the frailty varies over different covariate groups (e.g. male group versus female group). For such systematic heterogeneity in frailty, we consider a regression model for the variance components in the proportional hazards frailty model, denoted by the MLFM. However, in many cases, the observed data do not show any statistically significant preference between the homogeneous frailty model and the heterogeneous frailty model. In this paper, we propose a Bayesian model averaging procedure with the reversible jump Markov chain Monte Carlo which selects the appropriate model automatically. The resulting regression coefficient estimate ignores the model uncertainty from the frailty distribution in view of Bayesian model averaging (Hoeting et al., 1999). Finally, the proposed model and the estimation procedure are illustrated through the analysis of the kidney infection data in McGilchrist and Aisbett (1991) and a simulation study is implemented.
Keywords
Heterogeneous frailty; kidney infection data; multi-level regression model; proportional hazards model; variance components;
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