将误分类的EHR结果与非概率样本的验证结果相整合
Integrating Misclassified EHR Outcomes with Validated Outcomes from a Non-probability Sample
AI总结：
提出新型估计量整合存在测量误差的EHR数据与非概率验证样本的金标准结果，以纠正选择偏差并降低平均处理效应估计的偏差，提升推断效率。
摘要：
尽管电子健康记录（EHR）越来越多地被用于研究，但它们通常缺乏对关键数据要素的金标准评估。将EHR与具有更高质量测量的其他数据源进行链接可以改善统计推断，但如果链接的数据源来自非概率样本，此类分析必须考虑选择偏差。我们提出了一组针对平均处理效应（ATE）的新型估计量，该估计量结合了来自大型、人群代表性EHR数据库中存在测量误差的二元结果信息，以及来自存在选择偏差的较小验证样本的金标准结果。我们在大量模拟以及对Adult Changes in Thought（ACT）研究数据的分析中评估了我们的方法。ACT是一项针对Kaiser Permanente Washington成员队列中首发痴呆的纵向研究，并带有链接的EHR数据。对于在死亡前同意进行脑部尸检的已故ACT参与者子集，提供了阿尔茨海默病神经病理学的金标准测量。我们提出的估计量减少了ATE的偏差并提高了效率，从而在关键数据要素存在测量误差时促进基于EHR数据的有效推断。
英文摘要：
Although increasingly used for research, electronic health records (EHR) often lack gold-standard assessment of key data elements. Linking EHRs to other data sources with higher-quality measurements can improve statistical inference, but such analyses must account for selection bias if the linked data source arises from a non-probability sample. We propose a set of novel estimators targeting the average treatment effect (ATE) that combine information from binary outcomes measured with error in a large, population-representative EHR database with gold-standard outcomes obtained from a smaller validation sample subject to selection bias. We evaluate our approach in extensive simulations and an analysis of data from the Adult Changes in Thought (ACT) study, a longitudinal study of incident dementia in a cohort of Kaiser Permanente Washington members with linked EHR data. For a subset of deceased ACT participants who consented to brain autopsy prior to death, gold-standard measures of Alzheimer's disease neuropathology are available. Our proposed estimators reduced bias and improved efficiency for the ATE, facilitating valid inference with EHR data when key data elements are ascertained with error.
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