A product-limit estimator of the conditional survival function when cure status is partially known

We introduce a nonparametric estimator of the conditional survival function in the mixture cure model for right-censored data when cure status is partially known. The estimator is developed for the setting of a single continuous covariate but it can be extended to multiple covariates. It extends the estimator of Beran, which ignores cure status information. We obtain an almost sure representation, from which the strong consistency and asymptotic normality of the estimator are derived. Asymptotic expressions of the bias and variance demonstrate a reduction in the variance with respect to Beran's estimator. A simulation study shows that, if the bandwidth parameter is suitably chosen, our estimator performs better than others for an ample range of covariate values. A bootstrap bandwidth selector is proposed. Finally, the proposed estimator is applied to a real dataset studying survival of sarcoma patients.

Automated summary

In this study, a nonparametric estimator for the conditional survival function in the mixture cure model is introduced for right-censored data with partial knowledge of cure status. The estimator, developed for a single continuous covariate, can be extended to multiple covariates. Results show a reduction in variance compared to previous estimators and better performance across a range of covariate values when the bandwidth parameter is suitably chosen.