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Cox regression analysis with missing covariates via nonparametric multiple imputation.

Chiu-Hsieh Hsu1,2, Mandi Yu3

  • 11 Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.

Statistical Methods in Medical Research
|May 3, 2018
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Summary
This summary is machine-generated.

This study introduces a novel nonparametric multiple imputation method for Cox regression with missing covariates. The approach is robust to model misspecification, outperforming other methods in simulations and real-world data analysis.

Keywords:
Augmented inverse probability weighted methodCox regressionmissing covariatesmultiple imputationpredictive mean matching

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Missing Data Methods

Background:

  • Estimating Cox regression models is crucial for survival analysis.
  • Missing covariates can introduce significant bias in these estimations.
  • Existing methods like predictive mean matching imputation (PMM) and augmented inverse probability weighted (AIPW) have limitations.

Purpose of the Study:

  • To develop a robust imputation method for Cox regression with non-ignorantly missing covariates.
  • To evaluate the performance of the proposed method against existing techniques.
  • To address the challenges posed by missing covariate data in survival analysis.

Main Methods:

  • Proposed a nonparametric multiple imputation approach using two working regression models.
  • Developed a nearest neighbor imputing set for nonparametric imputation of missing covariate values.
  • Performed Cox regression on multiply imputed datasets to estimate coefficients.
  • Compared the proposed method with PMM and AIPW using simulation studies.

Main Results:

  • All compared methods, including the proposed one, reduced bias from non-ignorable missing mechanisms.
  • The proposed nonparametric imputation method demonstrated robustness to working model and link function misspecification.
  • PMM was sensitive to covariate misspecification in imputation.
  • AIPW was sensitive to the selection probability.

Conclusions:

  • The proposed nonparametric multiple imputation method offers a robust alternative for Cox regression with missing covariates.
  • This method is less sensitive to model misspecification compared to PMM and AIPW.
  • The approach was successfully applied to a real-world breast cancer dataset from the SEER Program.