ANALYSIS MULTILEVEL SURVIVAL DATA USING COVARIATE-ADJUSTED FRAILTY PROPORTIONAL HAZARDS MODEL

Multilevel survival data is time-to-event data with a hierarchical or nested structure. This study aims to model the data using the Covariate-Adjusted Frailty Proportional Hazards method, which is an extension of the Cox proportional hazards model with the addition of random effects (frailty). Parameter estimation is performed using a Bayesian approach via Markov Chain Monte Carlo (MCMC). This method is applied to analyze repeated observations of Chronic Granulomatous Disease (CGD) infections, with frailty represented by the hospital and the patient. The results of the data analysis indicate that both hospital and patient frailty significantly influence the time to infection, with patient frailty having a greater effect. Additionally, the treatment variable rINF-g significantly in reducing the risk of serious infection for CGD patients by 64.44%.