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Shared inverse Gaussian frailty models based on additive hazards

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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 analyze the bivariate data on related survival times (e.g., matched pairs experiments, twin, or family data), the shared frailty models were suggested. These models are based on the assumption that frailty acts multiplicatively to hazard rate. In this article, we assume that frailty acts additively to hazard rate. We introduce the shared inverse Gaussian frailty models with three different baseline distributions, namely the generalized log-logistic, the generalized Weibull, and exponential power distribution. We introduce the Bayesian estimation procedure using Markov chain Monte Carlo technique to estimate the parameters involved in these models. We apply these models to a real-life bivariate survival dataset of McGilchrist and Aisbett (1991) related to the kidney infection data, and a better model is suggested for the data.

Suggested Citation

  • David D. Hanagal & Arvind Pandey, 2017. "Shared inverse Gaussian frailty models based on additive hazards," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(22), pages 11143-11162, November.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:22:p:11143-11162
    DOI: 10.1080/03610926.2016.1260740
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    Cited by:

    1. 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.
    2. Zhongwen Zhang & Xiaoguang Wang & Yingwei Peng, 2022. "An additive hazards frailty model with semi-varying coefficients," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(1), pages 116-138, January.

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