Gamma shared frailty model based on reversed hazard rate for bivariate survival data

The unknown or unobservable risk factors in the survival analysis cause heterogeneity between the individuals. Frailty models are used in the survival analysis to account for the unobserved heterogeneity in the individual risks to disease and death. In this paper, we suggest the shared gamma frailty model with the reversed hazard rate. We introduce the Bayesian estimation procedure using MCMC technique to estimate the parameters involved in the model and compare the frailty model with the baseline model. We apply the proposed models to Australian twin data set and suggest a better model.