Parametric Analysis of Bivariate Current Status data with Competing risks using Frailty model

TL;DR

This paper introduces two new Gamma frailty models for bivariate current status data with competing risks, investigates their identifiability, and compares their performance using simulations and real data analysis.

Contribution

The paper proposes novel shared cause-specific and correlated cause-specific Gamma frailty models for competing risks data, expanding existing modeling approaches.

Findings

New frailty models are identifiable under certain conditions.

Simulation studies show finite sample properties of estimators.

Real data analysis demonstrates improved model fit with new frailty models.

Abstract

Shared and correlated Gamma frailty models are widely used in the literature to model the association in multivariate current status data. In this paper, we have proposed two other new Gamma frailty models, namely shared cause-specific and correlated cause-specific Gamma frailty to capture association in bivariate current status data with competing risks. We have investigated the identifiability of the bivariate models with competing risks for each of the four frailty variables. We have considered maximum likelihood estimation of the model parameters. Thorough simulation studies have been performed to study the finite sample behaviour of the estimated parameters. Also, we have analyzed a real data set on hearing loss in two ears using Exponential type and Weibull type cause-specific baseline hazard functions with the four different Gamma frailty variables and compare the fits using AIC.