Modelling bivariate survival data based on reversed hazard rate
by David D. Hanagal; Arvind Pandey
International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO), Vol. 6, No. 1, 2015

Abstract: Frailty models are used in the survival analysis to account for the unobserved heterogeneity in individual risks to disease and death. To analyse the bivariate data on related survival times (e.g., matched pairs experiments, twin or family data), the shared frailty models were suggested. The shared frailty models are frequently used to model heterogeneity in survival analysis. The most common shared frailty model is a model in which hazard function is a product of random factor (frailty) and the baseline hazard function which is common to all individuals. There are certain assumptions about the baseline distribution and distribution of frailty. In this paper, we introduce the shared gamma frailty models with the reversed hazard rate. We introduce the Bayesian estimation procedure using the Markov Chain Monte Carlo (MCMC) technique to estimate the parameters involved in the model. We present a simulation study to compare the true values of the parameters with the estimated values. Also, we apply the proposed model to Australian twin dataset and suggest a better model.

Online publication date: Sun, 19-Apr-2015

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