时间：6月21日下午15:30
地点：统计楼103
报告人：张佳佳（University of South Carolina）
报告题目：Analysis of Longitudinal Data with Informative Observation and Terminal Event Times
摘要：
For semiparametric survival models with interval censored data and a cure fraction, it is often difficult to derive nonparametric maximum likelihood estimation due to the challenge in maximizing the complex likelihood function. In this paper, we propose a computationally efficient EM algorithm, facilitated by a gamma-poisson data augmentation, for maximum likelihood estimation in a class of generalized odds rate mixture cure (GORMC) models with interval censored data. The gamma-poisson data augmentation greatly simplifies the EM estimation and enhances the convergence speed of the EM algorithm. The empirical properties of the proposed method are examined through extensive simulation studies and compared with numerical maximum likelihood estimates. An R package ``GORCure is developed to implement the proposed method and its use is illustrated by an application to the Aerobic Center Longitudinal Study dataset.