A Comparative Study of Shared Frailty Models for Kidney Infection Data with Generalized Exponential Baseline Distribution

Abstract

Shared frailty models are often used to model heterogeneity in survival analysis. The most common shared frailty model is a model in which hazard function is a product of random factor (frailty) and baseline hazard function which is common to all individuals. There are certain assumptions about the baseline distribution and distribution of frailty. Mostly assumption of gamma distribution is considered for frailty distribution. To compare the results with gamma frailty model, we introduce three shared frailty models with generalized exponential as baseline distribution. The other three shared frailty models are inverse Gaussian shared frailty model, compound Poisson shared frailty model and compound negative binomial shared frailty model. We fit these models to a real life bivariate survival data set of McGilchrist and Aisbett (1991) related to kidney infection using Markov Chain Monte Carlo (MCMC) technique. Model comparison is made using Bayesian model selection criteria and a better model is suggested for the data.

Influence diagnostics in the inverse Gaussian ridge regression model: Applications in chemometrics

“…Ducharme 28 developed some goodness-of-fit tests for the IGRM. Hanagal and Dabade 29 investigated the frailty model for bivariate survival data about the IGRM.…”