Generalised Lindley shared additive frailty regression model for bivariate survival data

Pandey Arvind (), Hanagal David D. () and Tyagi Shikhar ()
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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 .

Statistics in Transition New Series, 2022, vol. 23, issue 4, 161-176

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.

Keywords: Bayesian estimation; frailty; generalised Lindley frailty; generalised log-logistic distribution; generalised Weibull distribution; hazard rate; MCMC; random censoring (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:23:y:2022:i:4:p:161-176:n:7

DOI: 10.2478/stattrans-2022-0048

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