In 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.

中文翻译：

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在可能错误指定的测量误差模型下使用受误差污染的生存数据进行估计和假设检验

在存在协变量测量误差的情况下，人们对开发与各种生存模型相关的参数的估计方法产生了广泛的兴趣，其中经典的加性测量误差模型通常用于描述测量误差过程。相反，对于具有错误污染协变量的生存数据，假设检验的探索较少。此外，重要的是研究测量误差过程的错误指定的影响。在本文中，我们提出了“校正”分数测试和“校正” Wald 测试，并建立了它们的理论属性。此外，我们利用测量误差模型的错误指定对参数估计和假设检验的影响。模拟研究报告证明了所提出方法的有限样本性能，