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Optimal subsampling for semi-parametric accelerated failure time models with massive survival data using a rank-based

Zehan Yang1, HaiYing Wang1, Jun Yan1

  • 1Department of Statistics, University of Connecticut, Storrs, Connecticut, USA.

Statistics in Medicine
|August 20, 2024
PubMed
Summary

This study introduces an optimal subsampling method for analyzing large survival datasets using semi-parametric accelerated failure time (AFT) models. The new approach improves accuracy and variance estimation for survival data analysis.

Keywords:
A‐optimalitystochastic processsurvival analysis

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Subsampling is crucial for analyzing large survival datasets.
  • Optimal subsampling is established for Cox and parametric AFT models.
  • Limited research exists for semi-parametric AFT models, especially with rank-based estimation.

Purpose of the Study:

  • To develop an optimal subsampling method for semi-parametric accelerated failure time (AFT) models.
  • To address challenges with non-smooth estimating functions and censored observations.
  • To provide a feasible and accurate estimation method for large-scale survival data.

Main Methods:

  • Developed optimal subsampling probabilities for event and censored observations.
  • Utilized a stochastic process to define estimating functions.
  • Applied an induced smoothing procedure to non-smooth estimating functions.
  • Employed a two-step procedure for feasible coefficient estimation.

Main Results:

  • The proposed method effectively handles non-smooth estimating functions and censored data.
  • The two-step procedure yields feasible and accurate regression coefficient estimates.
  • The method corrects variance underestimation issues in subsampling.
  • Validated through simulation studies and real-world lymphoma patient data.

Conclusions:

  • The developed optimal subsampling method is effective for semi-parametric AFT models.
  • This approach enhances the analysis of large survival datasets.
  • The method offers improved accuracy and reliable variance estimation.