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Optimal distributed subsampling for accelerated failure time models with massive censored data.

Chunjie Wang1, Jing Li1, Xiaohui Yuan1

  • 1School of Mathematics and Statistics, Changchun University of Technology, Changchun, People's Republic of China.

Journal of Applied Statistics
|December 10, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a distributed subsampling method for accelerated failure time (AFT) models, improving analysis of large, multi-site datasets. The approach ensures accurate estimations for survival analysis in complex data environments.

Keywords:
62-02Accelerated failure time modelbig datadistributed and massive dataoptimal allocation sizessubsampling probabilities

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Area of Science:

  • Biostatistics
  • Data Science
  • Survival Analysis

Background:

  • Increasing availability of massive, multi-location datasets across various scientific fields.
  • Existing research often overlooks distributed datasets with censored observations.
  • Accelerated Failure Time (AFT) models are valuable for intuitive survival time interpretation.

Purpose of the Study:

  • To develop a distributed subsampling procedure for Accelerated Failure Time (AFT) models.
  • To address challenges in analyzing large-scale, decentralized survival data.
  • To provide a statistically sound and practically implementable method for distributed survival analysis.

Main Methods:

  • Development of a novel distributed subsampling procedure tailored for AFT models.
  • Theoretical validation including proofs of consistency and asymptotic normality for the proposed estimator.
  • A two-step algorithm designed for practical implementation, optimizing subsampling probabilities and allocation sizes.

Main Results:

  • The proposed distributed subsampling method demonstrates consistency and asymptotic normality.
  • A practical two-step algorithm is presented for efficient implementation and parameter optimization.
  • Numerical simulations confirm the method's effectiveness in performance evaluation.

Conclusions:

  • The developed distributed subsampling procedure is effective for AFT model analysis on large, multi-site datasets.
  • The method offers a statistically rigorous and practically applicable solution for distributed survival data.
  • The approach was successfully applied to a real-world lymphoma dataset, demonstrating its utility.