School of Public and Community Health Sciences, University of Montana, Missoula, Montana, USA.
Institute for Health Research, Kaiser Permanente Colorado, Aurora, Colorado, USA.
J Am Med Inform Assoc. 2019 Dec 1;26(12):1664-1674. doi: 10.1093/jamia/ocz094.
In health informatics, there have been concerns with reuse of electronic health data for research, including potential bias from incorrect or incomplete outcome ascertainment. In this tutorial, we provide a concise review of predictive value-based quantitative bias analysis (QBA), which comprises epidemiologic methods that use estimates of data quality accuracy to quantify the bias caused by outcome misclassification.
Health informaticians and investigators reusing large, electronic health data sources for research.
When electronic health data are reused for research, validation of outcome case definitions is recommended, and positive predictive values (PPVs) are the most commonly reported measure. Typically, case definitions with high PPVs are considered to be appropriate for use in research. However, in some studies, even small amounts of misclassification can cause bias. In this tutorial, we introduce methods for quantifying this bias that use predictive values as inputs. Using epidemiologic principles and examples, we first describe how multiple factors influence misclassification bias, including outcome misclassification levels, outcome prevalence, and whether outcome misclassification levels are the same or different by exposure. We then review 2 predictive value-based QBA methods and why outcome PPVs should be stratified by exposure for bias assessment. Using simulations, we apply and evaluate the methods in hypothetical electronic health record-based immunization schedule safety studies. By providing an overview of predictive value-based QBA, we hope to bridge the disciplines of health informatics and epidemiology to inform how the impact of data quality issues can be quantified in research using electronic health data sources.
在健康信息学中，人们一直关注电子健康数据在研究中的再利用问题，包括由于不正确或不完整的结果确定而导致的潜在偏差。在本教程中，我们提供了基于预测值的定量偏差分析（QBA）的简明回顾，该分析包括使用数据质量准确性估计来量化由结果分类错误引起的偏差的流行病学方法。
重新使用大型电子健康数据源进行研究的健康信息学和调查人员。
当电子健康数据被重新用于研究时，建议对结果定义进行验证，并且阳性预测值（PPV）是最常报告的衡量标准。通常，具有高 PPV 的病例定义被认为适用于研究。然而，在某些研究中，即使少量的分类错误也可能导致偏差。在本教程中，我们引入了使用预测值作为输入的量化这种偏差的方法。我们使用流行病学原理和示例，首先描述了多个因素如何影响分类错误偏差，包括结果分类错误水平、结果流行率以及结果分类错误水平是否因暴露而相同或不同。然后，我们回顾了 2 种基于预测值的 QBA 方法，以及为什么应该根据暴露情况对结果 PPV 进行分层以进行偏差评估。我们通过模拟应用和评估了这些方法，这些方法适用于基于电子健康记录的免疫接种计划安全性研究。通过提供基于预测值的 QBA 概述，我们希望弥合健康信息学和流行病学之间的学科差距，以了解如何在使用电子健康数据源的研究中量化数据质量问题的影响。
