ArticleJournal of the American Medical Informatics Association : JAMIA2023
Healthcare utilization is a collider: an introduction to collider bias in EHR data reuse.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Equity and Cancer Survival Among Veterans Health Administration Patients: A Systematic Review and Meta-Analysis.JAMA network open · 2026Pooled it
- Observational studies of early versus late salvage therapies in critical care exhibit intrinsic selection bias: two meta-analyses.Critical care (London, England) · 2025Pooled it
- Leveraging electronic health record data to evidence collider stratification bias and inform clinical epidemiology: obesity, diabetes, and rotator cuff tears.American journal of epidemiology · 2026Article
- Spatial bias in quality of estimated delivery date records in the electronic health record.JAMIA open · 2026Article
- AI Cautionary Guide: Pitfalls and Strategies for the Use of Machine Learning in Medical Research.Clinical pharmacology and therapeutics · 2026Review
- Beyond Missingness: Systematizing Methods for Comprehensive Data Fitness Assessment in Clinical Research.Journal of medical Internet research · 2026Article
- Transforming neonatal care through informatics: A review of artificial intelligence, data, and implementation considerations.Seminars in perinatology · 2025Review
- Racial disparities in diabetes care and outcomes for people with visual impairment: a descriptive analysis of the TriNetX research network.BMC public health · 2025Article
- Review and Comparative Evaluation of Mobile Apps for Cardiovascular Risk Estimation: Usability Evaluation Using mHealth App Usability Questionnaire.JMIR mHealth and uHealth · 2025Review
- Utilization of Computable Phenotypes in Electronic Health Record Research: A Review and Case Study in Atopic Dermatitis.The Journal of investigative dermatology · 2025Review
- Identifying commonalities and differences between EHR representations of PASC and ME/CFS in the RECOVER EHR cohort.Communications medicine · 2025Article
- Investigating Primary Care Indications to Improve the Quality of Electronic Health Record Data in Target Trial Emulation for Dementia.medRxiv : the preprint server for health sciences · 2025Article
- Step-by-step causal analysis of EHRs to ground decision-making.PLOS digital health · 2025Article
- With big data comes big responsibility: Strategies for utilizing aggregated, standardized, de-identified electronic health record data for research.Clinical and translational science · 2025Article
- A Common Longitudinal Intensive Care Unit data Format (CLIF) to enable multi-institutional federated critical illness research.medRxiv : the preprint server for health sciences · 2024Article
- Understanding enterprise data warehouses to support clinical and translational research: impact, sustainability, demand management, and accessibility.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Explaining COVID-19 related mortality disparities in American Indians and Alaska Natives.Scientific reports · 2023Article
- Dementia and electronic health record phenotypes: a scoping review of available phenotypes and opportunities for future research.Journal of the American Medical Informatics Association : JAMIA · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectivesCollider bias is a common threat to internal validity in clinical research but is rarely mentioned in informatics education or literature. Conditioning on a collider, which is a variable that is the shared causal descendant of an exposure and outcome, may result in spurious associations between the exposure and outcome. Our objective is to introduce readers to collider bias and its corollaries in the retrospective analysis of electronic health record (EHR) data. TARGET AUDIENCE: Collider bias is likely to arise in the reuse of EHR data, due to data-generating mechanisms and the nature of healthcare access and utilization in the United States. Therefore, this tutorial is aimed at informaticians and other EHR data consumers without a background in epidemiological methods or causal inference. SCOPE: We focus specifically on problems that may arise from conditioning on forms of healthcare utilization, a common collider that is an implicit selection criterion when one reuses EHR data. Directed acyclic graphs (DAGs) are introduced as a tool for identifying potential sources of bias during study design and planning. References for additional resources on causal inference and DAG construction are provided.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.