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Shared frailty models based on reversed hazard rate for modified inverse Weibull distribution as baseline distribution

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  • David D. Hanagal
  • Arvind Pandey

Abstract

The unknown or unobservable risk factors in the survival analysis cause heterogeneity between individuals. Frailty models are used in the survival analysis to account for the unobserved heterogeneity in individual risks to disease and death. To analyze the bivariate data on related survival times, the shared frailty models were suggested. The most common shared frailty model is a model in which frailty act multiplicatively on the hazard function. In this paper, we introduce the shared gamma frailty model and the inverse Gaussian frailty model with the reversed hazard rate. We introduce the Bayesian estimation procedure using 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. We also apply the proposed models to the Australian twin data set and a better model is suggested.

Suggested Citation

  • David D. Hanagal & Arvind Pandey, 2017. "Shared frailty models based on reversed hazard rate for modified inverse Weibull distribution as baseline distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(1), pages 234-246, January.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:1:p:234-246
    DOI: 10.1080/03610926.2014.990102
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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.

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