A Support Vector Machine Based Semiparametric Mixture Cure Model

• Activity Number: 270 - Nonparametric and Semiparametric Statistical Inference for Cure Models
• Type: Invited
• Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
• Sponsor: Journal of Nonparametric Statistics
• Author(s): Yingwei Peng* and Peizhi Li and Qingli Dong
• Companies: Queen's University and Dongbei University of Finance and Economics and Queen's University and Dongbei University of Finance and Economics and Queen's University
• Keywords: Censored survival time; Cure model; Support vector machine; EM algorithm; Multiple imputation

Abstract

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.