{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T09:08:42Z","timestamp":1773220122837,"version":"3.50.1"},"reference-count":33,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Clinical & Translational All"],"published-print":{"date-parts":[[2025,8]]},"abstract":"ABSTRACT<\/jats:title>Background<\/jats:title>Robust data are essential for clinical and epidemiological research, yet in chronic spontaneous urticaria (CSU), certain patient groups, such as the elderly or comorbid patients, are often underrepresented. In clinical trials, strict inclusion and exclusion criteria frequently limit recruitment, making it difficult to achieve sufficient statistical power. Similarly, real\u2010world observational studies may lack sufficient sample sizes for robust analysis. To address these limitations, we generated synthetic patient data that reflect these groups\u2019 clinical characteristics and variability. This approach enables more comprehensive analyses, facilitates hypothesis testing in otherwise inaccessible populations, and supports the generation of evidence where traditional data sources are insufficient.<\/jats:p><\/jats:sec>Methods<\/jats:title>A tree\u2010based decision model was applied to generate synthetic data based on an existing set of real\u2010world data (RWD) from the Chronic Urticaria Registry (CURE). Descriptive characteristics and association strength between relevant RWD variables and their synthetic counterparts were analyzed as indicators of replication accuracy, providing insight into how closely the synthetic data aligns with the RWD. Finally, we determined the minimum sample size required to generate high\u2010quality synthetic data.<\/jats:p><\/jats:sec>Results<\/jats:title>The algorithm produced extensive synthetic data records, closely mirroring patient demographics and disease clinical characteristics. Smaller subgroups of the data were equally replicated and followed the same distribution as RWD. Known associations and correlations between disease\u2010specific factors (disease control) and risk factors (age) yielded similar results, with no significant difference (p<\/jats:italic>\u00a0>\u00a00.05). The lowest threshold at which synthetic data could be generated while maintaining high accuracy in RWD was identified to be 25%, enabling a fourfold increase in the synthetic population.<\/jats:p><\/jats:sec>Conclusion<\/jats:title>Synthetic data could replicate RWD with reasonable accuracy for patients with CSU down to 25% of the original population size. This method has the potential to extend small patient subgroups in clinical and epidemiological research.<\/jats:p><\/jats:sec>","DOI":"10.1002\/clt2.70087","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T07:50:22Z","timestamp":1754553022000},"source":"Crossref","is-referenced-by-count":1,"title":["Can Synthetic Data Allow for Smaller Sample Sizes in Chronic Urticaria Research?"],"prefix":"10.1002","volume":"15","author":[{"given":"Annika","family":"Gutsche","sequence":"first","affiliation":[{"name":"Institute of Allergology Charit\u00e9\u2014Universit\u00e4tsmedizin Berlin Corporate Member of Freie Universit\u00e4t Berlin and Humboldt\u2010Universit\u00e4t zu Berlin Berlin Germany"},{"name":"Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Immunology and Allergology Berlin Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pascale","family":"Salameh","sequence":"additional","affiliation":[{"name":"Institute of Allergology Charit\u00e9\u2014Universit\u00e4tsmedizin Berlin Corporate Member of Freie Universit\u00e4t Berlin and Humboldt\u2010Universit\u00e4t zu Berlin Berlin Germany"},{"name":"Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Immunology and Allergology Berlin Germany"},{"name":"Faculty of Pharmacy Lebanese University Beirut Lebanon"},{"name":"Gilbert and Rose\u2013Marie Chagoury School of Medicine Lebanese American University Beirut Lebanon"},{"name":"Department of Primary Care and Population Health University of Nicosia Medical School Nicosia Cyprus"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samad S.","family":"Jahandideh","sequence":"additional","affiliation":[{"name":"Tediax B.V. Sterrenbos 5 Valburg the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehran","family":"Roodsaz","sequence":"additional","affiliation":[{"name":"Tediax B.V. Sterrenbos 5 Valburg the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Serkan","family":"Kutan","sequence":"additional","affiliation":[{"name":"Tediax B.V. 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