Modeling bivariate survival data using shared inverse Gaussian frailty model

被引:15
作者
Hanagal, David D. [1 ]
Bhambure, Susmita M. [1 ]
机构
[1] Univ Pune, Dept Stat, Pune, Maharashtra, India
关键词
Bayesian estimation; Copula; Inverse Gaussian distribution; Markov Chain Monte Carlo; Model selection criterion; Shared frailty; EXPONENTIATED WEIBULL FAMILY; X DISTRIBUTION; HETEROGENEITY; DISTRIBUTIONS; MORTALITY;
D O I
10.1080/03610926.2014.901380
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The shared frailty models are often used to model heterogeneity in survival analysis. The most common shared frailty model is a model in which hazard function is a product of a random factor (frailty) and the baseline hazard function which is common to all individuals. There are certain assumptions about the baseline distribution and the distribution of frailty. In this paper, we consider inverse Gaussian distribution as frailty distribution and three different baseline distributions, namely the generalized Rayleigh, the weighted exponential, and the extended Weibull distributions. With these three baseline distributions, we propose three different inverse Gaussian shared frailty models. We also compare these models with the models where the above-mentioned distributions are considered without frailty. We develop the Bayesian estimation procedure using Markov Chain Monte Carlo (MCMC) technique to estimate the parameters involved in these models. We present a simulation study to compare the true values of the parameters with the estimated values. A search of the literature suggests that currently no work has been done for these three baseline distributions with a shared inverse Gaussian frailty so far. We also apply these three models by using a real-life bivariate survival data set of McGilchrist and Aisbett (1991) related to the kidney infection data and a better model is suggested for the data using the Bayesian model selection criteria.
引用
收藏
页码:4969 / 4987
页数:19
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