Abstract
In this article, general inference procedures are proposed for the additive hazards model when covariates are subject to measurement errors and the errors are non-informative. The methods are not restricted to classical additive error models, but are capable of handling general covariate error structures. They can be applied to studies with either an external or internal validation sample, and also to studies with replicate measurements of the surrogate covariate. The asymptotic properties of the resulting estimators are derived, and simulation studies are conducted to evaluate the performance of the proposed estimators. A real example is provided.
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