A Support vector machine-based mixture cure model for mixed case interval censored data.

ABSTRACT: We propose a semi-parametric two-component model for the analysis of mixed case interval censored (MCIC) data with a cured subgroup. Such data occurs when the time to an event of interest is only known to belong to an interval obtained from a sequence of, say, k random examination time points with k representing an integer. Furthermore, there is a proportion of subjects who would never be susceptible to the event. The first component of the proposed model describes the probability of cure, and it replaces the traditional generalized linear model with a more flexible support vector machine (SVM)-based approach capable of capturing complex covariate effects. The second component of the proposed model describes the survival distribution of the uncured and is modeled using a Cox

SUBMITTER: Pal S 

PROVIDER: S-EPMC12811344 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature