Nonparametric estimation of conditional cure models for heavy-tailed distributions and under insufficient follow-up

When analyzing time-to-event data, it often happens that some subjects do not experience the event of interest. Survival models take this feature into account (called ‘cure models’) have been developed in presence covariates. However, nonparametric cure with covariates, current literature, cannot be applied when follow-up is insufficient, i.e., right endpoint support censoring time strictly smaller than survival susceptible subjects. New estimators conditional rate and function are proposed using extrapolation techniques coming from extreme value theory. The methodology can also used to estimate no present. asymptotic normality established their performances for small samples shown by means a simulation study. Their practical applicability illustrated through analysis two short applications real datasets on repayment student bullet loans employee's turnover company.