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The proportional hazards model with covariate measurement error. (English) Zbl 1074.62065

Summary: The proportional hazards regression model is commonly used to evaluate the relationship between survival and covariates. Covariates are frequently measured with error. Substituting mismeasured values for the true covariates leads to biased estimation. P. Hu et al. [Biometrics 54, 1407–1419 (1998; Zbl 1058.62557)] have proposed to base estimation in the proportional hazards model with covariate measurement error on a joint likelihood for survival and the covariate variable. Nonparametric maximum likelihood estimation (NPMLE) was used and simulations were conducted to assess the asymptotic validity of this approach. In this paper, we derive a rigorous proof of asymptotic normality of the NPML estimators.

MSC:

62N02 Estimation in survival analysis and censored data
62G05 Nonparametric estimation
62G20 Asymptotic properties of nonparametric inference
62N01 Censored data models
62G08 Nonparametric regression and quantile regression

Citations:

Zbl 1058.62557
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References:

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