Large‐scale association analyses based on observational health care databases such as electronic health records have been a topic of increasing interest in the scientific community. However, challenges due to nonprobability sampling and phenotype misclassification associated with the use of these data sources are often ignored in standard analyses. The extent of the bias introduced by ignoring these factors is not well‐characterized. In this paper, we develop an analytic framework for characterizing the bias expected in disease‐gene association studies based on electronic health records when disease status misclassification and the sampling mechanism are ignored. Through a sensitivity analysis approach, this framework can be used to obtain plausible values for parameters of interest given summary results from standard analysis. We develop an online tool for performing this sensitivity analysis. Simulations demonstrate promising properties of the proposed method. We apply our approach to study bias in disease‐gene association studies using electronic health record data from the Michigan Genomics Initiative, a longitudinal biorepository effort within The University Michigan health system.

中文翻译：

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使用电子健康记录探索基因组和表型关联研究中的采样和观察过程偏差的分析框架。

基于观察性医疗保健数据库（如电子健康记录）的大规模关联分析已成为科学界越来越感兴趣的话题。然而，由于与使用这些数据源相关的非概率抽样和表型错误分类带来的挑战在标准分析中经常被忽略。忽略这些因素导致的偏差程度尚未得到很好的表征。在本文中，我们开发了一个分析框架，用于在疾病状态错误分类和抽样机制被忽略时，基于电子健康记录来表征疾病基因关联研究中的预期偏差。通过敏感性分析方法，该框架可用于在给出汇总结果的情况下获得感兴趣参数的合理值从标准分析。我们开发了一个在线工具来执行这种敏感性分析。模拟证明了所提出方法的有希望的特性。我们使用来自密歇根基因组学计划的电子健康记录数据应用我们的方法来研究疾病基因关联研究中的偏差，该计划是密歇根大学卫生系统内的纵向生物存储库工作。