Medical studies that depend on electronic health records (EHR) data are often subject to measurement error, as the data are not collected to support research questions under study. These data errors, if not accounted for in study analyses, can obscure or cause spurious associations between patient exposures and disease risk. Methodology to address covariate measurement error has been well developed; however, time‐to‐event error has also been shown to cause significant bias, but methods to address it are relatively underdeveloped. More generally, it is possible to observe errors in both the covariate and the time‐to‐event outcome that are correlated. We propose regression calibration (RC) estimators to simultaneously address correlated error in the covariates and the censored event time. Although RC can perform well in many settings with covariate measurement error, it is biased for nonlinear regression models, such as the Cox model. Thus, we additionally propose raking estimators which are consistent estimators of the parameter defined by the population estimating equation. Raking can improve upon RC in certain settings with failure‐time data, require no explicit modeling of the error structure, and can be utilized under outcome‐dependent sampling designs. We discuss features of the underlying estimation problem that affect the degree of improvement the raking estimator has over the RC approach. Detailed simulation studies are presented to examine the performance of the proposed estimators under varying levels of signal, error, and censoring. The methodology is illustrated on observational EHR data on HIV outcomes from the Vanderbilt Comprehensive Care Clinic.

中文翻译：

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倾斜和回归校准：解决相关协变量和事件时间误差带来的偏差的方法

依赖电子健康记录 (EHR) 数据的医学研究经常会出现测量误差，因为收集的数据并不是为了支持正在研究的研究问题。如果研究分析中没有考虑到这些数据错误，可能会掩盖或导致患者暴露与疾病风险之间的虚假关联。解决协变量测量误差的方法已经得到很好的发展；然而，事件发生时间误差也被证明会导致显着偏差，但解决该问题的方法相对不发达。更一般地说，可以观察到相关的协变量和事件发生时间结果中的错误。我们提出回归校准（RC）估计器来同时解决协变量和审查事件时间中的相关误差。尽管 RC 在许多具有协变量测量误差的设置中表现良好，但它对于非线性回归模型（例如 Cox 模型）有偏差。因此，我们另外提出了 raking 估计器，它们是总体估计方程定义的参数的一致估计器。Raking 可以在某些具有故障时间数据的设置中改进 RC，不需要对错误结构进行显式建模，并且可以在结果相关的抽样设计下使用。我们讨论影响 raking 估计器相对 RC 方法改进程度的基本估计问题的特征。详细的模拟研究旨在检查所提出的估计器在不同水平的信号、误差和审查下的性能。该方法通过范德比尔特综合护理诊所的艾滋病毒结果观察性电子病历数据进行说明。