From the Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
Epidemiology. 2022 Jan 1;33(1):105-113. doi: 10.1097/EDE.0000000000001432.
Electronic health records (EHRs) offer unprecedented opportunities to answer epidemiologic questions. However, unlike in ordinary cohort studies or randomized trials, EHR data are collected somewhat idiosyncratically. In particular, patients who have more contact with the medical system have more opportunities to receive diagnoses, which are then recorded in their EHRs. The goal of this article is to shed light on the nature and scope of this phenomenon, known as informative presence, which can bias estimates of associations. We show how this can be characterized as an instance of misclassification bias. As a consequence, we show that informative presence bias can occur in a broader range of settings than previously thought, and that simple adjustment for the number of visits as a confounder may not fully correct for bias. Additionally, where previous work has considered only underdiagnosis, investigators are often concerned about overdiagnosis; we show how this changes the settings in which bias manifests. We report on a comprehensive series of simulations to shed light on when to expect informative presence bias, how it can be mitigated in some cases, and cases in which new methods need to be developed.
电子健康记录 (EHR) 为回答流行病学问题提供了前所未有的机会。然而，与普通的队列研究或随机试验不同，EHR 数据的收集有些特殊。特别是，与医疗系统接触更多的患者有更多机会接受诊断，这些诊断随后会被记录在他们的 EHR 中。本文的目的是阐明这种被称为信息性存在的现象的性质和范围，这种现象可能会对关联的估计产生偏差。我们展示了如何将其描述为一种分类错误偏倚。因此，我们表明，信息性存在偏差可能会出现在比以前想象的更广泛的环境中，并且简单地将就诊次数作为混杂因素进行调整可能无法完全纠正偏差。此外，虽然之前的研究仅考虑了漏诊，但研究人员通常也担心过度诊断；我们展示了这如何改变了出现偏差的环境。我们报告了一系列全面的模拟，以阐明何时可以预期信息性存在偏差，在某些情况下如何减轻偏差，以及在哪些情况下需要开发新方法。
