Estimation and hypothesis testing with error‐contaminated survival data under possibly misspecified measurement error models

Abstract

AbstractIn the presence of covariate measurement error, there has been extensive interest in developing estimation methods for parameters associated with various survival models, where the classical additive measurement error model is commonly used to describe the measurement error process. On the contrary, hypothesis testing has been less explored for survival data with error‐contaminated covariates. Furthermore, it is important to study the impact of misspecification of the measurement error process. In this article, we propose a “corrected” score test and a “corrected” Wald test and establish their theoretical properties. Moreover, we exploit the impact of misspecification of measurement error models on parameter estimation and hypothesis testing. Simulation studies are reported to demonstrate the finite‐sample performance of the proposed methods, and a real data example is presented to illustrate the usage of our methods.