Analysis of Bivariate Survival Data Using Shared Additive Hazard Gamma Frailty Models

Abstract

In this article, we propose additive hazard shared gamma frailty model with generalized Pareto, generalized Rayleigh and xgamma distributions as baseline distribution to analyze the bivariate survival data set of McGilchrist and Aisbett [16]. Assumption of the model is that frailty acts additively to hazard rate. The Bayesian approach of Markov Chain Monte Carlo technique was employed to estimate the parameters involved in the models. We present a simulation study to compare the true values of the parameters with the estimated values. Additive hazard shared gamma frailty model with generalized Pareto baseline distribution fits better than other propose models for kidney infection data.

Key words and phrases. Additive hazard, Bayesian method, gamma frailty, generalized Pareto distribution, generalized Rayleigh distribution, xgamma distribution.

2000 Mathematics Subject Classification. 62F15, 62N01, 62P10