Informative presence bias in analyses of electronic health records-derived data: a cautionary note.
Informative presence bias in analyses of electronic health records-derived data: a cautionary note.
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电子健康记录衍生数据分析中的信息存在偏差：警告。
DOI：
10.1093/jamia/ocac050
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Hubbard,RebeccaA
中科院分区：
文献类型：
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作者：
Harton,Joanna;Mitra,Nandita;Hubbard,RebeccaA
电子健康记录（EHR）衍生的数据广泛用于健康研究。然而，如果那些健康状况较差的患者比健康患者记录了更多的数据，则患者与医疗保健系统的交互模式可能导致信息存在偏差。我们的目的是确定信息存在如何影响偏见在多个场景下告知现实世界的医疗保健利用patterns.Materials and MethodsWe进行了分析的EHR数据从儿科医疗保健系统以及模拟研究的特征条件下，信息存在偏见可能发生。该分析通过检查生物标志物和感兴趣的健康事件之间的关系以及医疗保健访问过程的各种场景来扩展先前的工作。结果当估计生物标志物对感兴趣的事件的影响时，使用在信息性和非信息性访问中收集的生物标志物值，当生物标志物随着时间的推移相对稳定时，产生最小偏差，但当生物标志物更多时，产生实质性偏差。不稳定调整分析的数量在一个固定的回顾窗口内的先前访问是能够减少，但不能消除这种bias.DiscussionThese结果表明，偏差可能会经常出现在常见的情况下，并可能不会消除通过调整先前的访问intensity.ConclusionDepending的上下文，估计的效果从分析使用的数据从所有访问可用可能偏离真实的效果。根据访视类型，仅使用可能提供信息或不提供信息的访视进行敏感性分析，可能有助于评估潜在偏倚的程度。
ObjectiveElectronic health record (EHR)-derived data are extensively used in health research. However, the pattern of patient interaction with the healthcare system can result in informative presence bias if those who have poorer health have more data recorded than healthier patients. We aimed to determine how informative presence affects bias across multiple scenarios informed by real-world healthcare utilization patterns.Materials and methodsWe conducted an analysis of EHR data from a pediatric healthcare system as well as simulation studies to characterize conditions under which informative presence bias is likely to occur. This analysis extends prior work by examining a variety of scenarios for the relationship between a biomarker and a health event of interest and the healthcare visit process.ResultsUsing biomarker values gathered at both informative and noninformative visits when estimating the effect of the biomarker on the event of interest resulted in minimal bias when the biomarker was relatively stable over time but produced substantial bias when the biomarker was more volatile. Adjusting analyses for the number of prior visits within a fixed look-back window was able to reduce but not eliminate this bias.DiscussionThese results suggest that bias may arise frequently in commonly encountered scenarios and may not be eliminated by adjusting for prior visit intensity.ConclusionDepending on the context, the estimated effect from analyses using data from all visits available may diverge from the true effect. Sensitivity analyses using only visits likely to be informative or noninformative based on visit type may aid in the assessment of the magnitude of potential bias.