Correlated gamma frailty models for bivariate survival data based on reversed hazard rate

David D. Hanagal and Arvind Pandey

International Journal of Data Science, 2017, vol. 2, issue 4, 301-324

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. Shared frailty models are used despite their limitations. To overcome their disadvantages correlated frailty models may be used. In this paper, we introduce the gamma correlated frailty models based on reversed hazard rate (RHR) with three different baseline distributions namely, the generalised log-logistic type I, the generalised log-logistic type II and the modified inverse Weibull. We introduce 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. We also apply the proposed models to the Australian twin dataset and a better model is suggested.

Keywords: Bayesian estimation; correlated gamma frailty; generalised log-logistic distribution type I; generalised log-logistic type II; modified inverse Weibull distribution. (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdsci:v:2:y:2017:i:4:p:301-324

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