Biomedical studies that use electronic health records (EHR) data for inference are often subject to bias due to measurement error. The measurement error present in EHR data is typically complex, consisting of errors of unknown functional form in covariates and the outcome, which can be dependent. To address the bias resulting from such errors, generalized raking has recently been proposed as a robust method that yields consistent estimates without the need to model the error structure. We provide rationale for why these previously proposed raking estimators can be expected to be inefficient in failure-time outcome settings involving misclassification of the event indicator. We propose raking estimators that utilize multiple imputation, to impute either the target variables or auxiliary variables, to improve the efficiency. We also consider outcome-dependent sampling designs and investigate their impact on the efficiency of the raking estimators, either with or without multiple imputation. We present an extensive numerical study to examine the performance of the proposed estimators across various measurement error settings. We then apply the proposed methods to our motivating setting, in which we seek to analyze HIV outcomes in an observational cohort with EHR data from the Vanderbilt Comprehensive Care Clinic.

中文翻译：

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改进了广义倾斜估计器，以解决相关协变量和故障时间结果错误

使用电子健康记录 (EHR) 数据进行推断的生物医学研究经常会因测量误差而出现偏差。EHR 数据中存在的测量误差通常很复杂，由协变量和结果中未知函数形式的误差组成，这些误差可能是相关的。为了解决此类错误导致的偏差，最近提出了广义倾斜作为一种鲁棒方法，可以产生一致的估计，而无需对错误结构进行建模。我们提供了为什么这些先前提出的倾斜估计器在涉及事件指示器错误分类的故障时间结果设置中效率低下的原因。我们提出利用多重插补的耙式估计器来插补目标变量或辅助变量，以提高效率。我们还考虑了结果相关的抽样设计，并研究了它们对耙式估计器效率的影响，无论是否有多重插补。我们提出了广泛的数值研究，以检查所提出的估计器在各种测量误差设置下的性能。然后，我们将所提出的方法应用到我们的激励环境中，在该环境中，我们寻求利用范德比尔特综合护理诊所的 EHR 数据来分析观察队列中的 HIV 结果。