ArticleJournal of the American Medical Informatics Association : JAMIA2022
SAT: a Surrogate-Assisted Two-wave case boosting sampling method, with application to EHR-based association studies.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Overcoming data challenges through enriched validation and targeted sampling to measure whole-person health in electronic health records.Journal of biomedical informatics · 2025Article
- Prediction of traumatic hemorrhagic shock using a Multi-scale exogenous variable model (MS-TimeXer-MoE).European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2025Article
- A framework for understanding selection bias in real-world healthcare data.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- Scalable and interpretable alternative to chart review for phenotype evaluation using standardized structured data from electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices.Journal of machine learning research : JMLR · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectivesElectronic health records (EHRs) enable investigation of the association between phenotypes and risk factors. However, studies solely relying on potentially error-prone EHR-derived phenotypes (ie, surrogates) are subject to bias. Analyses of low prevalence phenotypes may also suffer from poor efficiency. Existing methods typically focus on one of these issues but seldom address both. This study aims to simultaneously address both issues by developing new sampling methods to select an optimal subsample to collect gold standard phenotypes for improving the accuracy of association estimation. MATERIALS AND
methodsWe develop a surrogate-assisted two-wave (SAT) sampling method, where a surrogate-guided sampling (SGS) procedure and a modified optimal subsampling procedure motivated from A-optimality criterion (OSMAC) are employed sequentially, to select a subsample for outcome validation through manual chart review subject to budget constraints. A model is then fitted based on the subsample with the true phenotypes. Simulation studies and an application to an EHR dataset of breast cancer survivors are conducted to demonstrate the effectiveness of SAT.
resultsWe found that the subsample selected with the proposed method contains informative observations that effectively reduce the mean squared error of the resultant estimator of the association.
conclusionsThe proposed approach can handle the problem brought by the rarity of cases and misclassification of the surrogate in phenotype-absent EHR-based association studies. With a well-behaved surrogate, SAT successfully boosts the case prevalence in the subsample and improves the efficiency of estimation.
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Registered trials
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