SUPPORT VECTOR MACHINE FOR DYNAMIC SURVIVAL PREDICTION WITH TIME-DEPENDENT COVARIATES

• 成果类型: Article
• 署名作者: Xie, Wenyi; Zeng, Donglin; Wang, Yuanjia
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of Michigan System; University of Michigan; Columbia University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS1875
• 发表日期: 2024
• 页码: 2166-2186
• 关键词: regression; trees; MODEL

摘要

Predicting time-to-event outcomes using time-dependent covariates is a challenging problem. Many machine learning approaches, such as tree-based methods and support vector regression, predominantly utilize only baseline covariates. Only a few methods can incorporate time-dependent covariates, but they often lack theoretical justification. In this paper we present a new framework for event time prediction, leveraging the support vector machines to forecast the associated counting processes. Utilizing the kernel trick, we accommodate nonlinear functions in both time and covariate spaces. Subsequently, we use a chain algorithm to predict future events. Theoretical analysis proves that our method is equivalent to comparing time-varying hazard rates among at-risk subjects, and we obtain the convergence rate of the resulting prediction loss. Through simulation studies and a case study on Huntington's disease, we demonstrate the superior performance of our approach compared to alternative methods based on machine learning, deep learning, and statistical models.