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

Annika Gutsche1,2, Pascale Salameh1,2,3,4,5,6, Samad S Jahandideh7

  • 1Institute of Allergology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.

Clinical and Translational Allergy
|August 7, 2025
PubMed
Summary

Synthetic data generation accurately reflects real-world data for chronic spontaneous urticaria (CSU) patients, even with smaller sample sizes. This approach enhances clinical and epidemiological research by including underrepresented patient groups.

Keywords:
chronic spontaneous urticaria (CSU)real‐world data (RWD)sensitivity analysissubgroup analysissynthetic data generationtree‐based decision model

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Area of Science:

  • Medical Informatics
  • Biostatistics
  • Epidemiology

Background:

  • Chronic spontaneous urticaria (CSU) research is limited by underrepresentation of elderly and comorbid patients in clinical trials and observational studies.
  • Strict trial criteria and small sample sizes hinder robust data analysis and statistical power.
  • Generating synthetic patient data can overcome these limitations by reflecting diverse clinical characteristics.

Purpose of the Study:

  • To develop and validate a method for generating synthetic patient data for chronic spontaneous urticaria (CSU).
  • To assess the accuracy of synthetic data in replicating real-world data (RWD) characteristics and associations.
  • To determine the minimum sample size required for high-quality synthetic data generation.

Main Methods:

  • A tree-based decision model was employed to generate synthetic data from existing RWD of the Chronic Urticaria Registry (CURE).
  • Replication accuracy was evaluated by analyzing descriptive characteristics and variable associations between RWD and synthetic data.
  • The minimum sample size threshold for maintaining high synthetic data accuracy was identified.

Main Results:

  • The synthetic data closely mirrored patient demographics and clinical characteristics of the RWD.
  • Subgroup replication and distributions aligned with RWD, with no significant differences in disease-specific factors and risk factors (p > 0.05).
  • High-accuracy synthetic data generation was achieved with as little as 25% of the original RWD, enabling a fourfold population increase.

Conclusions:

  • Synthetic data can accurately replicate RWD for CSU patients, even with reduced original population sizes.
  • This methodology offers a viable solution for augmenting small patient subgroups in clinical and epidemiological research.
  • Synthetic data generation holds significant potential for advancing research in underrepresented populations.