ArticleDrug safety2013
Replication of the OMOP experiment in Europe: evaluating methods for risk identification in electronic health record databases.
Article in Drug safety, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 3 of them syntheses that pooled it.
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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
29 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Methods for drug safety signal detection using routinely collected observational electronic health care data: A systematic review.Pharmacoepidemiology and drug safety · 2023Pooled it
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- Performance of the Self-Controlled Case Series With Active Comparators for Drug Safety Signal Detection Using the Clinical Practice Research Datalink (CPRD).Pharmacoepidemiology and drug safety · 2025Article
- Performance of the Self-Controlled Case Series With Active Comparators for Drug Safety Signal Detection Using the French Administrative Healthcare Database (SNDS).Pharmacoepidemiology and drug safety · 2025Article
- Performance of the Self-Controlled Case Series With Active Comparators for Drug Safety Signal Detection Using Merative MarketScan Research Databases.Pharmacoepidemiology and drug safety · 2025Article
- Data Lake, Data Warehouse, Datamart, and Feature Store: Their Contributions to the Complete Data Reuse Pipeline.JMIR medical informatics · 2024Article
- The State of Use and Utility of Negative Controls in Pharmacoepidemiologic Studies.American journal of epidemiology · 2024Review
- Implementation of a Taxonomy-Based Framework for the Selection of Appropriate Drugs and Outcomes for Real-World Data Signal Detection Studies.Drug safety · 2024Review
- Using public clinical trial reports to probe non-experimental causal inference methods.BMC medical research methodology · 2023Article
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- STaRT-RWE: structured template for planning and reporting on the implementation of real world evidence studies.BMJ (Clinical research ed.) · 2021Article
- How Confident Are We about Observational Findings in Healthcare: A Benchmark Study.Harvard data science review · 2020Article
- Transforming French Electronic Health Records into the Observational Medical Outcome Partnership's Common Data Model: A Feasibility Study.Applied clinical informatics · 2020Article
- UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Antibiotic prescriptions in acute otitis media and pharyngitis in Italian pediatric outpatients.Italian journal of pediatrics · 2019Observational
- An Automated System Combining Safety Signal Detection and Prioritization from Healthcare Databases: A Pilot Study.Drug safety · 2018Article
- Towards Implementation of OMOP in a German University Hospital Consortium.Applied clinical informatics · 2018Article
- Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0.Pharmacoepidemiology and drug safety · 2017Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe Observational Medical Outcomes Partnership (OMOP) has just completed a large scale empirical evaluation of statistical methods and analysis choices for risks identification in longitudinal observational healthcare data. This experiment drew data from four large US health insurance claims databases and one US electronic health record (EHR) database, but it is unclear to what extend the findings of this study apply to other data sources.
objectiveTo replicate the OMOP experiment in six European EHR databases. RESEARCH
designSix databases of the EU-ADR (Exploring and Understanding Adverse Drug Reactions) database network participated in this study: Aarhus (Denmark), ARS (Italy), HealthSearch (Italy), IPCI (the Netherlands), Pedianet (Italy), and Pharmo (the Netherlands). All methods in the OMOP experiment were applied to a collection of 165 positive and 234 negative control drug-outcome pairs across four outcomes: acute liver injury, acute myocardial infarction, acute kidney injury, and upper gastrointestinal bleeding. Area under the receiver operator characteristics curve (AUC) was computed per database and for a combination of all six databases using meta-analysis for random effects. We provide expected values of estimation error as well, based on negative controls.
resultsSimilarly to the US experiment, high predictive accuracy was found (AUC >0.8) for some analyses. Self-controlled designs, such as self-controlled case series, IC temporal pattern discovery and self-controlled cohort achieved higher performance than other methods, both in terms of predictive accuracy and observed bias.
conclusionsThe major findings of the recent OMOP experiment were also observed in the European databases.
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