Understanding synthetic data: artificial datasets for real-world evidence.
Saved in:
| 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 |
| ISSN: | 2515-4478 |
|---|---|
| DOI: | 10.1136/bmjebm-2024-113617 |
