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Generalised Lindley shared additive frailty regression model for bivariate survival data

Author

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  • Pandey Arvind

    (Department of Statistics, Central University of Rajasthan, Rajasthan, India .)

  • Hanagal David D.

    (Department of Statistics, Savitribai Phule Pune University, Pune, - 411007, India .)

  • Tyagi Shikhar

    (Department of Statistics, Central University of Rajasthan, Rajasthan, India .)

Abstract

Frailty models are the possible choice to counter the problem of the unobserved heterogeneity in individual risks of disease and death. Based on earlier studies, shared frailty models can be utilised in the analysis of bivariate data related to survival times (e.g. matched pairs experiments, twin or family data). In this article, we assume that frailty acts additively to the hazard rate. A new class of shared frailty models based on generalised Lindley distribution is established. By assuming generalised Weibull and generalised log-logistic baseline distributions, we propose a new class of shared frailty models based on the additive hazard rate. We estimate the parameters in these frailty models and use the Bayesian paradigm of the Markov Chain Monte Carlo (MCMC) technique. Model selection criteria have been applied for the comparison of models. We analyse kidney infection data and suggest the best model.

Suggested Citation

  • Pandey Arvind & Hanagal David D. & Tyagi Shikhar, 2022. "Generalised Lindley shared additive frailty regression model for bivariate survival data," Statistics in Transition New Series, Statistics Poland, vol. 23(4), pages 161-176, December.
    • Handle: RePEc:vrs:stintr:v:23:y:2022:i:4:p:161-176:n:7
    DOI: 10.2478/stattrans-2022-0048
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    References listed on IDEAS

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    1. 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.
    2. 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.
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    4. 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.
    5. Bacon, Robert W, 1993. "A Note on the Use of the Log-Logistic Functional Form for Modelling Saturation Effects," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 55(3), pages 355-361, August.
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