Computationally Efficient Estimation for the Generalized Odds Rate Mixture Cure Model With Interval-Censored Data
针对区间删失数据且存在治愈比例的半参数生存模型，提出一种基于伽马-泊松数据增强的EM算法，显著简化计算并提升收敛速度，并开发了R包GORCure。
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.