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A modified partial likelihood score method for Cox regression with covariate error under the internal validation

David M Zucker1, Xin Zhou2, Xiaomei Liao3

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We present a novel covariate error correction method for Cox survival regression, applicable to various error forms. This approach effectively reduces bias and improves confidence interval coverage in survival analysis.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Covariate measurement error is a common issue in observational studies.
  • Traditional survival regression models like Cox regression can be biased by covariate error.
  • Existing methods often struggle with complex or unknown error structures.

Purpose of the Study:

  • To develop a robust method for covariate error correction in Cox survival regression.
  • To address arbitrary forms of covariate error using internal validation data.
  • To improve the accuracy of survival analysis in the presence of measurement error.

Main Methods:

  • Developed a new statistical method for covariate error correction.
  • Derived asymptotic properties of the proposed estimator.
  • Utilized a modest sample of internal validation data for correction.

Main Results:

  • The method demonstrated excellent performance in bias reduction.
  • Achieved superior confidence interval coverage in simulation studies.
  • Successfully applied to real-world data from the Health Professionals Follow-Up Study.

Conclusions:

  • The new method provides accurate covariate error correction in Cox models.
  • It is effective for various error types and performs well in simulations.
  • Offers a valuable tool for analyzing diet and Type II diabetes incidence in epidemiological research.