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Non‐parametric cure rate estimation under insufficient follow‐up by using extremes

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  • Mikael Escobar‐Bach
  • Ingrid Van Keilegom

Abstract

An important research topic in survival analysis is related to the modelling and estimation of the cure rate, i.e. the proportion of subjects who will never experience the event of interest. However, most estimation methods proposed so far in the literature do not handle the case of insufficient follow‐up, i.e. when the right end point of the support of the censoring time is strictly less than that of the survival time of the susceptible subjects, and consequently these estimators overestimate the cure rate in that case. We fill this gap by proposing a new estimator of the cure rate that makes use of extrapolation techniques from the area of extreme value theory. We establish the asymptotic normality of the estimator proposed and show how the estimator works for small samples by means of a simulation study. We also illustrate its practical applicability through the analysis of data on the survival of breast cancer patients.

Suggested Citation

  • Mikael Escobar‐Bach & Ingrid Van Keilegom, 2019. "Non‐parametric cure rate estimation under insufficient follow‐up by using extremes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(5), pages 861-880, November.
    • Handle: RePEc:bla:jorssb:v:81:y:2019:i:5:p:861-880
    DOI: 10.1111/rssb.12334
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    1. Escobar-Bach, Mikael & Van Keilegom, Ingrid, 2023. "Nonparametric estimation of conditional cure models for heavy-tailed distributions and under insufficient follow-up," Computational Statistics & Data Analysis, Elsevier, vol. 183(C).

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