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Cox regression with dependent error in covariates.

Yijian Huang1, Ching-Yun Wang2

  • 1Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, U.S.A.

Biometrics
|July 7, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for survival analysis with measurement error in covariates. The approach handles complex error structures and provides reliable results, even with significant data contamination.

Keywords:
Functional modelingHeteroscedastic errorInstrumental variableMultiplicative errorNonparametric correctionProportional hazards model

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Measurement Error Models

Background:

  • Survival studies frequently encounter covariate measurement errors due to imperfect data collection.
  • These errors can exhibit complex, heteroscedastic patterns dependent on true covariate values, complicating analysis.

Purpose of the Study:

  • To develop a robust statistical method for Cox regression in the presence of dependent covariate measurement error.
  • To accommodate more general error structures than previously established methods.

Main Methods:

  • Proposed a novel dependent measurement error model with minimal assumptions on the error structure.
  • Introduced a functional modeling approach for Cox regression utilizing an instrumental variable.
  • Developed methods for consistent estimation of regression coefficients and variance.

Main Results:

  • The proposed method offers consistent and asymptotically normal estimates for regression coefficients.
  • A consistent variance estimation procedure is provided, enabling reliable statistical inference.
  • Simulation studies confirmed the method's effectiveness under substantial error contamination.

Conclusions:

  • The new functional modeling approach effectively addresses dependent covariate measurement error in survival analysis.
  • This method provides a valuable tool for analyzing data with complex error structures, enhancing reliability in clinical and observational studies.