The mixture cure model is an extension of standard survival models to analyze survival data with a cured fraction. Many developments in recent years focus on the latency part of the model to allow more flexible modeling strategies for the distribution of uncured subjects, and fewer studies focus on the incidence part to model the probability of being uncured/cured. We propose a new mixture cure model that employs the support vector machine (SVM) to model the covariate effects in the incidence part of the cure model. The new model inherits the features of the SVM to provide a flexible model to assess the effects of covariates on the incidence. Unlike the existing nonparametric approaches for the incidence part, the SVM method also allows for potentially high-dimensional covariates in the incidence part. Semiparametric models are also allowed in the latency part of the proposed model. We develop an estimation method to estimate the cure model and conduct a simulation study to show that the proposed model outperforms existing cure models, particularly in incidence estimation. An illustrative example using data from leukemia patients is given.

中文翻译：

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基于支持向量机的半参数混合固化模型

混合物治愈模型是标准生存模型的扩展，用于分析具有治愈分数的生存数据。近年来，许多发展都集中在模型的潜伏部分，以允许更灵活的建模策略来分配未固化的受试者，而较少的研究集中在发病率部分以建模未固化/治愈的概率。我们提出了一种新的混合固化模型，该模型采用支持向量机（SVM）来建模固化模型发生率部分中的协变量效应。新模型继承了SVM的功能，以提供灵活的模型来评估协变量对发病率的影响。与入射部分的现有非参数方法不同，SVM方法还允许入射部分中潜在的高维协变量。在建议模型的等待时间部分中还允许使用半参数模型。我们开发了一种估计方法来估计治愈模型，并进行了仿真研究，表明所提出的模型优于现有的治愈模型，尤其是在发生率估计方面。给出了使用来自白血病患者的数据的说明性实例。