论文标题
半参数混合物固化模型的两步估计程序
A 2-step estimation procedure for semiparametric mixture cure models
论文作者
论文摘要
已经开发了治疗模型作为常规生存分析的替代建模方法，以解释永远不会遇到感兴趣事件的固化受试者的存在。混合固化模型，该模型分别模拟了根据一组协变量的固化概率和未经许可受试者的存活，这对于将治愈性与生命效果区分开特别有用。在实践中，通常是假设治疗概率的参数模型和用于易感的生存的半参数模型。由于潜在的治疗状态，最大似然估计是通过迭代EM算法进行的。在这里，我们专注于治疗概率，并提出了两步程序，以改善当样本量不大时最大似然估计器的性能。新方法基于通过首先构建非参数估计器然后将其投影到所需的参数类中的预定介绍的想法。我们研究了所得估计器的理论特性，并通过对逻辑-Cox模型的广泛仿真研究表明，该研究表现优于现有方法。通过两个黑色素瘤数据集说明了该方法的实际使用。
Cure models have been developed as an alternative modelling approach to conventional survival analysis in order to account for the presence of cured subjects that will never experience the event of interest. Mixture cure models, which model separately the cure probability and the survival of uncured subjects depending on a set of covariates, are particularly useful for distinguishing curative from life-prolonging effects. In practice, it is common to assume a parametric model for the cure probability and a semiparametric model for the survival of the susceptibles. Because of the latent cure status, maximum likelihood estimation is performed by means of the iterative EM algorithm. Here, we focus on the cure probabilities and propose a two-step procedure to improve upon the performance of the maximum likelihood estimator when the sample size is not large. The new method is based on the idea of presmoothing by first constructing a nonparametric estimator and then projecting it into the desired parametric class. We investigate the theoretical properties of the resulting estimator and show through an extensive simulation study for the logistic-Cox model that it outperforms the existing method. Practical use of the method is illustrated through two melanoma datasets.
