Can Synthetic Data Allow for Smaller Sample Sizes in Chronic Urticaria Research?

Gutsche A. Salameh P. Jahandideh S.S. Roodsaz M. Kutan S. Salehzadeh-Yazdi A. Kocatürk E. Gregoriou S. Thomsen S.F. Kulthanan K. Tuchinda P. Dissemond J. Kasperska-Zajac A. Zajac M. Zamłyński M. van Doorn M. Parisi C.A.S. Peter J.G. Day C. McDougall C. Makris M. Fomina D. Kovalkova E. Streliaev N. Andrenova G. Lebedkina M. Khoskhkui M. Aliabadi M.M. Bauer A. Kiefer L. Muñoz M. Weller K. Kolkhir P. Metz M.
August 2025 John Wiley and Sons Inc

Clinical and Translational Allergy
2025 #15 Issue 8

Background: 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-world observational studies may lack sufficient sample sizes for robust analysis. To address these limitations, we generated synthetic patient data that reflect these groups’ 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. Methods: A tree-based decision model was applied to generate synthetic data based on an existing set of real-world 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-quality synthetic data. Results: 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-specific factors (disease control) and risk factors (age) yielded similar results, with no significant difference (p > 0.05). 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. Conclusion: 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.

chronic spontaneous urticaria (CSU) , real-world data (RWD) , sensitivity analysis , subgroup analysis , synthetic data generation , tree-based decision model

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Institute of Allergology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Immunology and Allergology, Berlin, Germany
Faculty of Pharmacy, Lebanese University, Beirut, Lebanon
Gilbert and Rose–Marie Chagoury School of Medicine, Lebanese American University, Beirut, Lebanon
Department of Primary Care and Population Health, University of Nicosia Medical School, Nicosia, Cyprus
Institut National de Santé Publique d’Épidémiologie Clinique et de Toxicologie-Liban (INSPECT-LB), Beirut, Lebanon
Tediax B.V. Sterrenbos 5, Valburg, Netherlands
School of Science, Constructor University, Bremen, Germany
Department of Dermatology, Bahçeşehir University School of Medicine, Istanbul, Turkey
Faculty of Medicine, National and Kapodistrian University of Athens, Andreas Sygros Hospital, Athens, Greece
Department of Dermatology, Bispebjerg Hospital, University of Copenhagen, Biomedical Sciences, Copenhagen, Denmark
Department of Dermatology, Faculty of Medicine Siriraj Hospital, Mahidol University Bangkok, Nakhon Pathom, Thailand
Department of Dermatology, Venerology and Allergology, University of Essen, Essen, Germany
Department of Clinical Allergology and Urticaria of Medical University of Silesia, Katowice, Poland
Department of Dermatology, Urticaria Center of Reference and Excellence, Erasmus MC, Rotterdam, Netherlands
Adults and Pediatric Allergy Sections, Italian Hospital of Buenos Aires, Buenos Aires, Argentina
Allergy and Immunology Unit, University of Cape Town Lung Institute, Cape Town, South Africa
Allergy Unit, 2nd Dpt. Dermatology and Venereology, National and Kapodistrian University of Athens, University General Hospital “Attikon”, Athens, Greece
Moscow Research and Practical Center of Allergy and Immunology, Moscow Healthcare Department, City Clinical Hospital, Moscow, Russian Federation
Department of Clinical Immunology and Allergology, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russian Federation
Department of Pulmonology, Astana Medical University, Astana, Kazakhstan
Allergy Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
Department of Dermatology, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany

Institute of Allergology
Fraunhofer Institute for Translational Medicine and Pharmacology ITMP
Faculty of Pharmacy
Gilbert and Rose–Marie Chagoury School of Medicine
Department of Primary Care and Population Health
Institut National de Santé Publique d’Épidémiologie Clinique et de Toxicologie-Liban (INSPECT-LB)
Tediax B.V. Sterrenbos 5
School of Science
Department of Dermatology
Faculty of Medicine
Department of Dermatology
Department of Dermatology
Department of Dermatology
Department of Clinical Allergology and Urticaria of Medical University of Silesia
Department of Dermatology
Adults and Pediatric Allergy Sections
Allergy and Immunology Unit
Allergy Unit
Moscow Research and Practical Center of Allergy and Immunology
Department of Clinical Immunology and Allergology
Department of Pulmonology
Allergy Research Center
Department of Dermatology

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