Regression Analysis for the Additive Hazards Model with Covariate Errors

This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. 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.