ArticleStatistics in medicine2022
Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification.
Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Canadian COVID-19 host genetics cohort replicates known severity associations.PLoS genetics · 2024Pooled it
- Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks.PLoS genetics · 2023Pooled it
- Article
- Selection processes, transportability, and failure time analysis in life history studies.Biostatistics (Oxford, England) · 2025Article
- Leveraging undecided cases in chart-reviewed phenotypes to enhance EHR-based association studies.Journal of biomedical informatics · 2025Article
- The impact of self-report inaccuracy in the UK Biobank and its interplay with selective participation.Nature human behaviour · 2025Article
- Methodological challenges in studying disease processes using observational cohort data.Japanese journal of statistics and data science · 2025Article
- A framework for understanding selection bias in real-world healthcare data.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- To weight or not to weight? The effect of selection bias in 3 large electronic health record-linked biobanks and recommendations for practice.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Systematic data quality assessment of electronic health record data to evaluate study-specific fitness: Report from the PRESERVE research study.PLOS digital health · 2024Article
- Biases in Electronic Health Records Data for Generating Real-World Evidence: An Overview.Journal of healthcare informatics research · 2024Article
- To weight or not to weight? Studying the effect of selection bias in three large EHR-linked biobanks.medRxiv : the preprint server for health sciences · 2024Article
- Bias correction models for electronic health records data in the presence of non-random sampling.Biometrics · 2024Article
- Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification.Statistics in medicine · 2022Article
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Abstract
Electronic health records (EHR) are not designed for population-based research, but they provide easy and quick access to longitudinal health information for a large number of individuals. Many statistical methods have been proposed to account for selection bias, missing data, phenotyping errors, or other problems that arise in EHR data analysis. However, addressing multiple sources of bias simultaneously is challenging. We developed a methodological framework (R package, SAMBA) for jointly handling both selection bias and phenotype misclassification in the EHR setting that leverages external data sources. These methods assume factors related to selection and misclassification are fully observed, but these factors may be poorly understood and partially observed in practice. As a follow-up to the methodological work, we demonstrate how to apply these methods for two real-world case studies, and we evaluate their performance. In both examples, we use individual patient-level data collected through the University of Michigan Health System and various external population-based data sources. In case study (a), we explore the impact of these methods on estimated associations between gender and cancer diagnosis. In case study (b), we compare corrected associations between previously identified genetic loci and age-related macular degeneration with gold standard external summary estimates. These case studies illustrate how to utilize diverse auxiliary information to achieve less biased inference in EHR-based research.
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