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Regression calibration in semiparametric accelerated failure time models.

Menggang Yu1, Bin Nan

  • 1Department of Medicine, Division of Biostatistics, Indiana University School of Medicine, 410 West 10th Street, Suite 3000, Indianapolis, Indiana 46202, USA. meyu@iupui.edu

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This summary is machine-generated.

This study extends regression calibration for survival data with measurement error, offering unbiased estimation for complex cohort studies. The method performs well in simulations and real-world applications.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Large cohort studies often face challenges with expensive covariate measurements, necessitating validation sets.
  • Error-prone covariates are available for all subjects, while precise measurements are limited to a subset.
  • Existing regression calibration (RC) methods are established for Cox models but require adaptation for other survival models.

Purpose of the Study:

  • To adapt the regression calibration (RC) estimation method for semiparametric accelerated failure time models.
  • To investigate the asymptotic properties of the proposed RC method under a two-phase sampling scheme.
  • To address challenges posed by measurement error in covariates within survival data analysis.

Main Methods:

  • The study applies regression calibration (RC) to the semiparametric accelerated failure time model.
  • Asymptotic properties are analyzed using a two-phase sampling scheme with stratified random sampling for validation data.
  • The method is evaluated through finite-sample simulation studies and applied to a real-world dataset.

Main Results:

  • The proposed RC method demonstrates convergence to well-defined parameters.
  • Unbiased estimation is achieved under additive normal measurement error for normal covariates and Berkson error models.
  • Simulation studies indicate good performance of the method in finite samples.

Conclusions:

  • The developed regression calibration method effectively handles measurement error in covariates for accelerated failure time models.
  • The approach provides a robust framework for analyzing complex survival data from large cohort studies.
  • The method is validated through simulations and a practical application in a depression mortality study.