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

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  • Hanagal, David D.
  • Pandey, Arvind

Abstract

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.

Suggested Citation

  • Hanagal, David D. & Pandey, Arvind, 2014. "Gamma shared frailty model based on reversed hazard rate for bivariate survival data," Statistics & Probability Letters, Elsevier, vol. 88(C), pages 190-196.
    • Handle: RePEc:eee:stapro:v:88:y:2014:i:c:p:190-196
    DOI: 10.1016/j.spl.2014.02.008
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    References listed on IDEAS

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    1. Hanagal David D., 2007. "Gamma Frailty Regression Models in Mixture Distributions," Stochastics and Quality Control, De Gruyter, vol. 22(2), pages 295-302, January.
    2. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
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    Cited by:

    1. Shikhar Tyagi & Arvind Pandey & Christophe Chesneau, 2022. "Weighted Lindley Shared Regression Model for Bivariate Left Censored Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 655-682, November.
    2. David D. Hanagal, 2022. "Correlated Positive Stable Frailty Models Based on Reversed Hazard Rate," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(1), pages 42-65, April.

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