Nonparametric correction for covariate measurement error in a stratified Cox model

Malka Gorfine, L. I. Hsu, Ross L. Prentice

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Stratified Cox regression models with large number of strata and small stratum size are useful in many settings, including matched case-control family studies. In the presence of measurement error in covariates and a large number of strata, we show that extensions of existing methods fail either to reduce the bias or to correct the bias under nonsymmetric distributions of the true covariate or the error term. We propose a nonparametric correction method for the estimation of regression coefficients, and show that the estimators are asymptotically consistent for the true parameters. Small sample properties are evaluated in a simulation study. The method is illustrated with an analysis of Framingham data.

Original languageEnglish
Pages (from-to)75-87
Number of pages13
JournalBiostatistics
Volume5
Issue number1
DOIs
StatePublished - Jan 2004

Keywords

  • Clayton-Oakes model
  • Framingham study
  • Matched case-control family study
  • Mismeasured covariates
  • Stratified censored data

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