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.