Analysis of kidney infection data using correlated compound poisson frailty models

Article type: Research Article

Authors: Hanagal, David D.

Affiliations: Department of Statistics, Savitribai Phule Pune University, Pune, India | E-mail: david.hanagal@gmail.com

Correspondence: [*] Corresponding author: Department of Statistics, Savitribai Phule Pune University, Pune, India. E-mail: david.hanagal@gmail.com.

Abstract: Shared frailty models are used despite their limitations. To overcome their disadvantages correlated frailty models may be used. In this paper, we introduce the correlated compound Poisson frailty models with two different baseline distributions namely, the generalized log logistic and the generalized Weibull. We introduce the Bayesian estimation procedure using Markov Chain Monte Carlo (MCMC) technique to estimate the parameters involved in these models. We present a simulation study to compare the true values of the parameters with the estimated values. Also we apply these models to a real life bivariate survival data set of McGilchrist and Aisbett (1991) related to the kidney infection data and a better model is suggested for the data.

Keywords: Bayesian estimation, correlated compound poisson frailty, generalized log-logistic distribution, generalized Weibull distribution, kidney infection data, model selection criteria

DOI: 10.3233/MAS-231452

Journal: Model Assisted Statistics and Applications, vol. 19, no. 2, pp. 159-171, 2024