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Conformal predictive intervals in survival analysis: a resampling approach.

Jing Qin1, Jin Piao2, Jing Ning3

  • 1Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institute of Health, Bethesda, MD 20892, United States.

Biometrics
|May 26, 2025
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Summary

This study introduces a new bootstrap method for conformal prediction with right-censored survival data, offering reliable prediction intervals for medical applications like breast cancer survival time prediction.

Keywords:
bootstrap samplingconformal predictionpredictive intervalright censored data

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Area of Science:

  • Statistics
  • Machine Learning
  • Medical Data Analysis

Background:

  • Conformal prediction is a powerful statistical tool.
  • Existing methods for right-censored survival data have limitations, especially in medical contexts.
  • General right-censored data presents unique challenges due to unobserved censoring times.

Purpose of the Study:

  • To develop a novel bootstrap method for constructing conformal predictive intervals for general right-censored survival data.
  • To address the limitations of existing methods in handling complex censoring patterns.
  • To provide reliable prediction intervals for medical applications, such as predicting patient survival times.

Main Methods:

  • A bootstrap method is proposed to construct 1- and 2-sided conformal predictive intervals.
  • The method is designed to work with general right-censored survival data.
  • Various working regression models are explored under the proposed framework.

Main Results:

  • The proposed method demonstrates excellent average coverage for lower prediction bounds.
  • Good coverage is observed for 2-sided predictive intervals, even when the working model is misspecified.
  • The method performs well, particularly under moderate censoring conditions.

Conclusions:

  • The bootstrap conformal prediction method is effective for general right-censored survival data.
  • This approach offers a robust solution for medical applications requiring survival time predictions.
  • The method was successfully applied to predict breast cancer patient survival times.