Bayesian estimation of generalized gamma shared frailty model

Sukhmani Sidhu (), Kanchan Jain () and Suresh Kumar Sharma ()
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Sukhmani Sidhu: Panjab University
Kanchan Jain: Panjab University
Suresh Kumar Sharma: Panjab University

Computational Statistics, 2018, vol. 33, issue 1, No 11, 277-297

Abstract: Abstract Multivariate survival analysis comprises of event times that are generally grouped together in clusters. Observations in each of these clusters relate to data belonging to the same individual or individuals with a common factor. Frailty models can be used when there is unaccounted association between survival times of a cluster. The frailty variable describes the heterogeneity in the data caused by unknown covariates or randomness in the data. In this article, we use the generalized gamma distribution to describe the frailty variable and discuss the Bayesian method of estimation for the parameters of the model. The baseline hazard function is assumed to follow the two parameter Weibull distribution. Data is simulated from the given model and the Metropolis–Hastings MCMC algorithm is used to obtain parameter estimates. It is shown that increasing the size of the dataset improves estimates. It is also shown that high heterogeneity within clusters does not affect the estimates of treatment effects significantly. The model is also applied to a real life dataset.

Keywords: Generalized gamma distribution; Gauss–Laguerre quadrature; Metropolis–Hastings algorithm; MCMC algorithm; Credible intervals (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s00180-017-0728-0

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