Zhou Jie, Zhang Jiajia, Lu Wenbin
Department of Epidemiology and Biostatistics, University of South Carolina.
Department of Statistics, North Carolina State University.
J Comput Graph Stat. 2018;27(1):48-58. doi: 10.1080/10618600.2017.1349665. Epub 2018 Feb 1.
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.
对于具有区间删失数据和治愈比例的半参数生存模型，由于最大化复杂似然函数存在挑战，通常难以推导非参数最大似然估计。在本文中，我们提出了一种计算效率高的期望最大化（EM）算法，通过伽马-泊松数据增广来实现，用于在一类具有区间删失数据的广义优势率混合治愈（GORMC）模型中进行最大似然估计。伽马-泊松数据增广极大地简化了EM估计，并提高了EM算法的收敛速度。通过广泛的模拟研究检验了所提方法的实证性质，并与数值最大似然估计进行了比较。开发了一个R包“GORCure”来实现所提方法，并通过应用于有氧中心纵向研究数据集来说明其用法。
