ABSTRACT 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 article, 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. Supplementary material for this article is available online.

中文翻译：

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具有区间删失数据的广义优势率混合固化模型的计算有效估计

摘要对于具有区间删失数据和治愈分数的半参数生存模型，由于最大化复杂似然函数的挑战，通常很难推导出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 EM 算法，由 gamma-Poisson 数据增强促进，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-泊松数据增强大大简化了EM估计并提高了EM算法的收敛速度。通过广泛的模拟研究并与数值最大似然估计进行比较，对所提出方法的经验特性进行了检查。开发了一个 R 包“GORCure”来实现所提出的方法，并通过对有氧中心纵向研究数据集的应用来说明其使用。本文的补充材料可在线获取。