论文名称：A support vector machine based semiparametric mixture cure model
论文类型：期刊论文
发表刊物：COMPUTATIONAL STATISTICS
收录刊物：SCIE
卷号：35
期号：3
页面范围：931-945
ISSN号：0943-4062
关键字：Censored survival time; Cure model; Support vector machine; EM algorithm; Multiple imputation
摘要：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.
发表时间：2020-09-01