It is well established that measurement error has drastically negative impact on data analysis. It can not only bias parameter estimates but may also cause loss of power for testing relationship between variables. Although survival analysis of error-contaminated data has attracted extensive interest, relatively little attention has been paid to dealing with survival data with error-contaminated covariates when the underlying population is characterized by a cured fraction. In this paper, we consider this problem for which lifetimes of the non-cured individuals are featured by the additive hazards model and the measurement error process is described by an additive model. Unlike estimating the relative risk in the proportional hazards model, the additive hazards model allows us to estimate the absolute risk difference associated with the covariates. To allow the model flexibility, we incorporate time-dependent covariates in the model. We develop estimation methods for the two scenarios, without or with measurement error. The proposed methods are evaluated from both the theoretical view point and the numerical perspectives. Furthermore, a real-life data application is presented to illustrate the utility of the methodology.

中文翻译：

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加性危害治愈率模型下带有测量误差的生存数据的半参数方法。

众所周知，测量误差对数据分析有极大的负面影响。它不仅可以使参数估计产生偏差，而且还可能导致检验变量之间关系的能力丧失。尽管错误污染数据的生存分析引起了广泛的兴趣，但当基础人群以治愈分数为特征时，处理带有错误污染协变量的生存数据的关注相对较少。在本文中，我们考虑了这个问题，即未治愈个体的寿命以加性风险模型为特征，测量误差过程由加性模型描述。与在比例风险模型中估计相对风险不同，加性风险模型允许我们估计与协变量相关的绝对风险差异。为了使模型具有灵活性，我们在模型中加入了与时间相关的协变量。我们为两种情况开发了估计方法，没有或有测量误差。从理论角度和数值角度对所提出的方法进行了评估。此外，还提供了一个真实的数据应用程序来说明该方法的实用性。