Modeling Australian Twin Data Using Generalized Lindley Shared Frailty Models

Pandey, Arvind; Hanagal, David D.; Tyagi, Shikhar; Gupta, Pragya Prashant

doi:10.1007/978-981-16-7932-2_10

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“…Pandey et al (2020b) looked at generalised inverse Gaussian shared frailty models based on reversed hazard rates. Pandey et al (2021aPandey et al ( , 2022 and Tyagi et al (2021a) developed distinct Generalised Lindley (GL) shared frailty models based on the reversed hazard rate. Tyagi et al (2021bTyagi et al ( , 2022aTyagi et al ( , 2022b, Gupta et al (2022), Pandey et al (2021b), and Pandey and Tyagi (2021) developed inverse weighted Lindley, and GL shared frailty models, respectively.…”

Section: Introductionmentioning

confidence: 99%

Generalised Lindley shared additive frailty regression model for bivariate survival data

Pandey

Hanagal

Tyagi

2022

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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.

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