  A simple yet effective way of modeling survival data with cure fraction is by
considering Box-Cox transformation cure model (BCTM) that unifies mixture and
promotion time cure models. In this article, we numerically study the
statistical properties of the BCTM when applied to interval censored data.
Time-to-events associated with susceptible subjects are modeled through
proportional hazards structure that allows for non-homogeneity across subjects,
where the baseline hazard function is estimated by distribution-free piecewise
linear function with varied degrees of non-parametricity. Due to missing cured
statuses for right censored subjects, maximum likelihood estimates of model
parameters are obtained by developing an expectation-maximization (EM)
algorithm. Under the EM framework, the conditional expectation of the complete
data log-likelihood function is maximized by considering all parameters
(including the Box-Cox transformation parameter $\alpha$) simultaneously, in
contrast to conventional profile-likelihood technique of estimating $\alpha$.
The robustness and accuracy of the model and estimation method are established
through a detailed simulation study under various parameter settings, and an
analysis of real-life data obtained from a smoking cessation study.
