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The proportional hazards model with covariate measurement error

Dupuy, Jean-François (2005), The proportional hazards model with covariate measurement error, Journal of Statistical Planning and Inference, 135, 2, p. 260-275. http://dx.doi.org/10.1016/j.jspi.2004.05.003

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Type
Article accepté pour publication ou publié
Date
2005
Journal name
Journal of Statistical Planning and Inference
Volume
135
Number
2
Publisher
Elsevier
Pages
260-275
Publication identifier
Metadata
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Author(s)
Dupuy, Jean-François
Abstract (EN)
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. Hu et al. (Biometrics 88 (1998) 447) 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.
Subjects / Keywords
Proportional hazards model; Covariate measurement error; Nonparametric maximum likelihood; Asymptotic normality