Compound Poisson Correlated Frailty Model Based on Modified Weibull Baseline Distribution for Bivariate Survival Data

Frailty models are survival models that are used to investigate the features of unobserved heterogeneity in people as it relating to disease and death. Despite their drawbacks, shared frailty models are frequently utilized. Several correlated frailty models were developed over the previous decade to solve these drawbacks. The performance of a compound Poisson correlated frailty model by considering modified Weibull distribution as the model baseline distribution is investigated in this work. The parameters in the proposed models are estimated by adopting Bayesian estimation procedure under the Markov chain Monte Carlo (MCMC) method. In addition, a comparison of the parameters' true values with estimated values is done using a simulation study.The data from Kidney infection was then used to test the proposed models. Models are compared to existing models using different information criteria and the Bayes factor. Accordingly, the best model for infected patient's data that have had their catheters inserted has been proposed.