  The mixture cure rate model is the most commonly used cure rate model in the
literature. In the context of mixture cure rate model, the standard approach to
model the effect of covariates on the cured or uncured probability is to use a
logistic function. This readily implies that the boundary classifying the cured
and uncured subjects is linear. In this paper, we propose a new mixture cure
rate model based on interval censored data that uses the support vector machine
(SVM) to model the effect of covariates on the uncured or the cured probability
(i.e., on the incidence part of the model). Our proposed model inherits the
features of the SVM and provides flexibility to capture classification
boundaries that are non-linear and more complex. Furthermore, the new model can
be used to model the effect of covariates on the incidence part when the
dimension of covariates is high. The latency part is modeled by a proportional
hazards structure. We develop an estimation procedure based on the expectation
maximization (EM) algorithm to estimate the cured/uncured probability and the
latency model parameters. Our simulation study results show that the proposed
model performs better in capturing complex classification boundaries when
compared to the existing logistic regression based mixture cure rate model. We
also show that our model's ability to capture complex classification boundaries
improve the estimation results corresponding to the latency parameters. For
illustrative purpose, we present our analysis by applying the proposed
methodology to an interval censored data on smoking cessation.
