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Understanding synthetic data: artificial datasets for real-world evidence.

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Title: Understanding synthetic data: artificial datasets for real-world evidence.
Authors: Foraker R; Department of Biomedical Informatics, Biostatistics, and Medical Epidemiology, University of Missouri, Columbia, Missouri, USA randi.foraker@health.missouri.edu., Morrow JD; Department of Obstetrics and Gynecology, New York University School of Medicine, New York, New York, USA., Johnson JA; Biological Sciences Division, The University of Chicago, Chicago, Illinois, USA., Wilcox AB; Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, Missouri, USA., Forster AJ; Department of Medicine, McGill University, Montréal, Québec, Canada., Payne PRO; Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, Missouri, USA.
Source: BMJ evidence-based medicine [BMJ Evid Based Med] 2026 May 21; Vol. 31 (3), pp. 148-151. Date of Electronic Publication: 2026 May 21.
Publication Type: Journal Article
Journal Info: Publisher: BMJ Publishing Group Country of Publication: England NLM ID: 101719009 Publication Model: Electronic Cited Medium: Internet ISSN: 2515-4478 (Electronic) Linking ISSN: 2515446X NLM ISO Abbreviation: BMJ Evid Based Med Subsets: MEDLINE; In Process
Database: MEDLINE Ultimate
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