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Cure rate estimation with insufficient follow-up: A median-based bootstrap correction approach

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  • Yumiko Ibi
  • Takashi Omori

Abstract

The cure rate in clinical trials can be estimated using the Kaplan–Meier (KM) estimator. However, when the clinical trial follow-up period is insufficient and short, the KM estimator may overestimate the proportion of cured patients. Although a correction method was proposed by Escobar-Bach and Keilegom based on bootstrap sampling, this can also lead to bias when the bootstrap distribution is skewed. We propose a median-based approach for bootstrap samples to address these issues. Simulation results showed that the effect of the variation of the proposed method due to many outliers was smaller than that of the other method and enabled stable estimation. The method was successfully applied to real clinical trial data from a D-penicillamine study on primary biliary cirrhosis.

Suggested Citation

  • Yumiko Ibi & Takashi Omori, 2026. "Cure rate estimation with insufficient follow-up: A median-based bootstrap correction approach," PLOS ONE, Public Library of Science, vol. 21(3), pages 1-12, March.
    • Handle: RePEc:plo:pone00:0344669
    DOI: 10.1371/journal.pone.0344669
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    References listed on IDEAS

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    1. Yilong Zhang & Xiaoxia Han & Yongzhao Shao, 2021. "The ROC of Cox proportional hazards cure models with application in cancer studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(2), pages 195-215, April.
    2. Yingwei Peng & Keith B. G. Dear, 2000. "A Nonparametric Mixture Model for Cure Rate Estimation," Biometrics, The International Biometric Society, vol. 56(1), pages 237-243, March.
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