ArticleJournal of the American Medical Informatics Association : JAMIA2020
Principles of Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND).
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
36 citing papers in PubMed.
- Evaluating large-scale propensity score adjustment when sample size is small.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Harmonising Pharmacovigilance and Pharmacoepidemiology: Lessons from Europe for Cohesive Medicines Safety Science.Drug safety · 2026Article
- Assessment of real-world evidence research competencies in federated research networks: a maternal health fellowship program evaluation.JAMIA open · 2026Article
- When evidence meets artificial intelligence.Lancet regional health. Americas · 2026Review
- Trust in Observational Research.Journal of the American College of Cardiology · 2026Article
- Real-world evidence for comparative safety of second-line antihyperglycemic agents in older adults with type 2 diabetes.Nature communications · 2026Article
- Preparing real-world data through common data model harmonization of cancer patient records in the COMNet platform at the Modena Oncology Center.Frontiers in digital health · 2026Article
- Assessing Covariate Balance With Small Sample Sizes.Statistics in medicine · 2025Article
- Assessing Covariate Balance with Small Sample Sizes.medRxiv : the preprint server for health sciences · 2025Article
- Objective study validity diagnostics: a framework requiring pre-specified, empirical verification to increase trust in the reliability of real-world evidence.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Risk of aortic aneurysm or dissection following use of fluoroquinolones: a retrospective multinational network cohort study.EClinicalMedicine · 2025Article
- Advancing Medical Imaging Research Through Standardization: The Path to Rapid Development, Rigorous Validation, and Robust Reproducibility.Investigative radiology · 2025Review
- The necessity of validity diagnostics when drawing causal inferences from observational data: lessons from a multi-database evaluation of the risk of non-infectious uveitis among patients exposed to RemicadeBMC medical research methodology · 2024Article
- Comparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes: A Multinational, Federated Analysis of LEGEND-T2DM.Journal of the American College of Cardiology · 2024Article
- Inter hospital external validation of interpretable machine learning based triage score for the emergency department using common data model.Scientific reports · 2024Article
- Seamless EMR data access: Integrated governance, digital health and the OMOP-CDM.BMJ health & care informatics · 2024Review
- The Stanford Medicine data science ecosystem for clinical and translational research.JAMIA open · 2023Article
- Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record.The American journal of cardiology · 2023Review
- A lifecycle framework illustrates eight stages necessary for realizing the benefits of patient-centered clinical decision support.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Serially Combining Epidemiological Designs Does Not Improve Overall Signal Detection in Vaccine Safety Surveillance.Drug safety · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Evidence derived from existing health-care data, such as administrative claims and electronic health records, can fill evidence gaps in medicine. However, many claim such data cannot be used to estimate causal treatment effects because of the potential for observational study bias; for example, due to residual confounding. Other concerns include P hacking and publication bias. In response, the Observational Health Data Sciences and Informatics international collaborative launched the Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) research initiative. Its mission is to generate evidence on the effects of medical interventions using observational health-care databases while addressing the aforementioned concerns by following a recently proposed paradigm. We define 10 principles of LEGEND that enshrine this new paradigm, prescribing the generation and dissemination of evidence on many research questions at once; for example, comparing all treatments for a disease for many outcomes, thus preventing publication bias. These questions are answered using a prespecified and systematic approach, avoiding P hacking. Best-practice statistical methods address measured confounding, and control questions (research questions where the answer is known) quantify potential residual bias. Finally, the evidence is generated in a network of databases to assess consistency by sharing open-source analytics code to enhance transparency and reproducibility, but without sharing patient-level information. Here we detail the LEGEND principles and provide a generic overview of a LEGEND study. Our companion paper highlights an example study on the effects of hypertension treatments, and evaluates the internal and external validity of the evidence we generate.
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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.