Compound negative binomial multivariate correlated frailty model for long-term survivors

Abstract

In the correlated frailty models, it is believed that all the individuals in the population will eventually experience the event of interest. This may not always be the situation in reality. There may be a certain fraction of the population which is immune to the event and hence may not experience the event under study. For such individuals, frailty may not be active i.e., zero. Hence, frailty distribution should provide a positive mass at zero. In this article, a new correlated frailty model with compound negative binomial distribution as frailty distribution, is introduced to incorporate the non-susceptibility of individuals. The inferential problem is solved in a Bayesian framework using Markov Chain Monte Carlo methods. The proposed model is then applied to a real life data set.

Keywords:

• Bayesian estimation
• Compound negative binomial distribution
• Correlated frailty
• Generalized exponential distribution
• MCMC algorithm

Aknowledgements

The author is thankful to the Editor and the referees for their comments and suggestions for improvements.

Funding

Author haven’t received any grant for research.