Inference in the additive risk model with time-varying covariates subject to measurement errors

For the additive risk model with time-varying covariates which are subject to measurement errors, we study the estimation of both regression parameters and cumulative baseline hazard function. We first develop a procedure to estimate the regression parameters by correcting the bias of the naive estimator, and provide the large-sample properties of the bias-adjusted estimators. The procedure can be repeated to further improve the accuracy of the estimator. We then construct a corresponding estimator for the cumulative baseline hazard function and derive its asymptotic properties. Based on these results, confidence bands are constructed for the cumulative hazard function as well as the survival function. Monte Carlo studies are conducted to evaluate the performance of these estimators.