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

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  • David D. Hanagal
  • Arvind Pandey

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.

Suggested Citation

  • David D. Hanagal & Arvind Pandey, 2017. "Correlated gamma frailty models for bivariate survival data based on reversed hazard rate," International Journal of Data Science, Inderscience Enterprises Ltd, vol. 2(4), pages 301-324.
    • Handle: RePEc:ids:ijdsci:v:2:y:2017:i:4:p:301-324
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    Cited by:

    1. Moustafa, Kassem & Hu, Zhen & Mourelatos, Zissimos P. & Baseski, Igor & Majcher, Monica, 2021. "System reliability analysis using component-level and system-level accelerated life testing," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    2. Pandey Arvind & Hanagal David D. & Tyagi Shikhar, 2022. "Generalised Lindley shared additive frailty regression model for bivariate survival data," Statistics in Transition New Series, Polish Statistical Association, vol. 23(4), pages 161-176, December.

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