Comparisons of frailty models for kidney infection data under Weibull baseline distribution
by David D. Hanagal; Alok D. Dabade
International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO), Vol. 5, No. 4, 2014

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

Online publication date: Fri, 31-Oct-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email