A voluminous literature on right-censored failure time data has been developed in the past 30 years. Due to advances in biomedical research, interval censoring has become increasingly common in medical follow-up studies. Additional problems arise in the analysis of multivariate interval-censored failure time data. I will present regression analysis of multivariate interval-censored failure time data using the frailty model approach. Based on the most commonly used regression model, the proportional hazards model, the frailty model approach considers the random effect directly models the correlation between multivariate failure times. The analysis is focused on current status or case I interval-censored data and the maximum likelihood approach is developed for inference. The simulation studies are conducted to evaluate and compare the finite-sample behaviors of the estimators and we apply the proposed method to an animal tumorigenicity experiment.
第十八屆南區統計研討會=The 18th South Taiwan Statistics Conference