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Biostatistics 5:75-87 (2004)
© 2004 Oxford University Press

Nonparametric correction for covariate measurement error in a stratified Cox model

Malka Gorfine*, Li Hsu and Ross L. Prentice

Malka Gorfine. Department of Mathematics and Statistics, Bar-Ilan University, Ramat-Gan 52900, Israel
Li Hsu, Ross L. Prentice. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA

*To whom correspondence should be addressed.

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.

Keywords: Clayton–Oakes model; Framingham study; Matched case-control family study; Mismeasured covariates; Stratified censored data


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