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A semiparametric copula method for Cox models with covariate measurement error

Lifetime Data Analysis volume 22, pages 1–16 (2016)Cite this article

Abstract

We consider measurement error problem in the Cox model, where the underlying association between the true exposure and its surrogate is unknown, but can be estimated from a validation study. Under this framework, one can accommodate general distributional structures for the error-prone covariates, not restricted to a linear additive measurement error model or Gaussian measurement error. The proposed copula-based approach enables us to fit flexible measurement error models, and to be applicable with an internal or external validation study. Large sample properties are derived and finite sample properties are investigated through extensive simulation studies. The methods are applied to a study of physical activity in relation to breast cancer mortality in the Nurses’ Health Study.

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Acknowledgments

The authors gratefully acknowledge the support of NIH/NCI Grant R01 CA050597 and NIH/NIEHS Grant R01 ES009411.

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Affiliations

  1. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

    Sehee Kim

  2. Department of Biostatistics, University of Michigan Kidney Epidemiology and Cost Center, University of Michigan, Ann Arbor, MI, USA

    Yi Li

  3. Departments of Epidemiology, Biostatistics, and Nutrition, Harvard University, Boston, MA, USA

    Donna Spiegelman

Authors
  1. Sehee Kim
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  2. Yi Li
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  3. Donna Spiegelman
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Corresponding author

Correspondence to Sehee Kim.

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Kim, S., Li, Y. & Spiegelman, D. A semiparametric copula method for Cox models with covariate measurement error. Lifetime Data Anal 22, 1–16 (2016). https://doi.org/10.1007/s10985-014-9315-7

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  • DOI: https://doi.org/10.1007/s10985-014-9315-7

Keywords

  • Bias correction
  • Copula
  • Error-prone covariate
  • Measurement error
  • Semiparametric
  • Survival analysis