Accurate outcome and exposure ascertainment in electronic health record (EHR) data, referred to as EHR phenotyping, relies on the completeness and accuracy of EHR data for each individual. However, some individuals, such as those with a greater comorbidity burden, visit the health care system more frequently and thus have more complete data, compared with others. Ignoring such dependence of exposure and outcome misclassification on visit frequency can bias estimates of associations in EHR analysis. We developed a framework for describing the structure of outcome and exposure misclassification due to informative visit processes in EHR data and assessed the utility of a quantitative bias analysis approach to adjusting for bias induced by informative visit patterns. Using simulations, we found that this method produced unbiased estimates across all informative visit structures, if the phenotype sensitivity and specificity were correctly specified. We applied this method in an example where the association between diabetes and progression-free survival in metastatic breast cancer patients may be subject to informative presence bias. The quantitative bias analysis approach allowed us to evaluate robustness of results to informative presence bias and indicated that findings were unlikely to change across a range of plausible values for phenotype sensitivity and specificity. Researchers using EHR data should carefully consider the informative visit structure reflected in their data and use appropriate approaches such as the quantitative bias analysis approach described here to evaluate robustness of study findings.

中文翻译：

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电子健康记录中信息存在偏差的定量偏差分析方法。


电子健康记录 (EHR) 数据中准确的结果和暴露确定（称为 EHR 表型分析）依赖于每个人的 EHR 数据的完整性和准确性。然而，与其他人相比，一些人，例如那些合并症负担较大的人，更频繁地访问医疗保健系统，因此拥有更完整的数据。忽视暴露和结果错误分类对就诊频率的这种依赖性可能会使 EHR 分析中关联的估计产生偏差。我们开发了一个框架，用于描述由于 EHR 数据中的信息性访问过程而导致的结果结构和暴露错误分类，并评估了定量偏差分析方法在调整信息性访问模式引起的偏差方面的效用。通过模拟，我们发现，如果正确指定表型敏感性和特异性，该方法可以对所有信息访问结构产生无偏估计。我们在一个例子中应用了这种方法，其中糖尿病与转移性乳腺癌患者的无进展生存期之间的关联可能会受到信息存在偏差的影响。定量偏倚分析方法使我们能够评估结果对信息存在偏倚的稳健性，并表明结果不太可能在表型敏感性和特异性的一系列合理值中发生变化。使用 EHR 数据的研究人员应仔细考虑其数据中反映的信息访问结构，并使用适当的方法（例如此处描述的定量偏差分析方法）来评估研究结果的稳健性。