A support vector machine based semiparametric mixture cure model

Peizhi Li, Yingwei Peng (), Ping Jiang and Qingli Dong
Additional contact information
Peizhi Li: Dongbei University of Finance and Economics
Yingwei Peng: Queen’s University
Ping Jiang: Dongbei University of Finance and Economics
Qingli Dong: Dalian University of Technology

Computational Statistics, 2020, vol. 35, issue 3, No 2, 945 pages

Abstract: 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.

Keywords: Censored survival time; Cure model; Support vector machine; EM algorithm; Multiple imputation (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00180-019-00931-w

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