Health research using electronic health records (EHR) has gained popularity, but misclassification of EHR-derived disease status and lack of representativeness of the study sample can result in substantial bias in effect estimates and can impact power and type I error. In this paper, we develop new strategies for handling disease status misclassification and selection bias in EHR-based association studies. We first focus on each type of bias separately. For misclassification, we propose three novel likelihood-based bias correction strategies. A distinguishing feature of the EHR setting is that misclassification may be related to patient-varying factors, and the proposed methods leverage data in the EHR to estimate misclassification rates without gold standard labels. For addressing selection bias, we describe how calibration and inverse probability weighting methods from the survey sampling literature can be extended and applied to the EHR setting.

中文翻译：

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使用电子健康记录进行关联研究的统计推断：处理选择偏差和结果错误分类

使用电子健康记录 (EHR) 的健康研究已广受欢迎，但对 EHR 衍生疾病状态的错误分类和研究样本缺乏代表性可能导致效果估计存在重大偏差，并可能影响功效和 I 型错误。在本文中，我们开发了新的策略来处理基于 EHR 的关联研究中的疾病状态错误分类和选择偏差。我们首先分别关注每种类型的偏差。对于错误分类，我们提出了三种新的基于可能性的偏差校正策略。EHR 设置的一个显着特征是错误分类可能与患者不同的因素有关，并且所提出的方法利用 EHR 中的数据来估计没有金标准标签的错误分类率. 为了解决选择偏差，我们描述了如何将调查抽样文献中的校准和逆概率加权方法扩展并应用于 EHR 设置。