mlsanityData to 14 Oct 2026

Generating Reliable Synthetic Clinical Trial Data: The Role of Hyperparameter Optimization and Domain Constraints

arXiv 2505.05019 ↗First posted 8 May 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

The generation of synthetic clinical trial data offers a promising approach to mitigating privacy concerns and data accessibility limitations in medical research. However, ensuring that synthetic datasets maintain high fidelity, utility, and adherence to domain-specific constraints remains a key challenge. While hyperparameter optimization (HPO) improves generative model performance, the effectiveness of different optimization strategies for synthetic clinical data remains unclear. This study systematically evaluates four HPO objectives across nine generative models, comparing single-metric to compound metric optimization. Our results demonstrate that HPO consistently improves synthetic data quality, with Tab DDPM achieving the largest relative gains, followed by TVAE (60%), CTGAN (39%), and CTAB-GAN+ (38%). Compound metric optimization outperformed single-metric objectives, producing more generalizable synthetic datasets. Despite improving overall quality, HPO alone fails to prevent violations of essential clinical survival constraints. Preprocessing and postprocessing played a crucial role in reducing these violations, as models lacking robust processing steps produced invalid data in up to 61% of cases. These findings underscore the necessity of integrating explicit domain knowledge alongside HPO to generate high-quality synthetic datasets. Our study provides actionable recommendations for improving synthetic data generation, with future work needed to refine metric selection and validate findings on larger datasets.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 52 references that are in the map. First posted on arXiv 8 May 2025.

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References · 26

  1. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  2. 2015Variational Inference with Normalizing Flowscited by 2,170
  3. 2019Modeling Tabular data using Conditional GANcited by 699
  4. 2021Tabular Data: Deep Learning is Not All You Needcited by 535
  5. 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 377
  6. 2022Language Models are Realistic Tabular Data Generatorscited by 294
  7. 2021How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Modelscited by 196
  8. 2023Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performancecited by 147
  9. 2018Tunability: Importance of Hyperparameters of Machine Learning Algorithmscited by 99
  10. 2023Synthcity: facilitating innovative use cases of synthetic data in different data modalitiescited by 76
  11. 2022CTAB-GAN+: Enhancing Tabular Data Synthesiscited by 70
  12. 2022Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overviewcited by 37
14 more
  1. 2024How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Datacited by 29
  2. 2022TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Datacited by 27
  3. 2023Synthetic Data Applications in Financecited by 22
  4. 2024Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analyticscited by 19
  5. 2024Systematic Assessment of Tabular Data Synthesiscited by 18
  6. 2023SurvivalGAN: Generating Time-to-Event Data for Survival Analysiscited by 17
  7. 2024Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcarecited by 17
  8. 2021Fidelity and Privacy of Synthetic Medical Datacited by 16
  9. 2020Robust Variational Autoencoder for Tabular Data with Beta Divergencecited by 13
  10. 2023CuTS: Customizable Tabular Synthetic Data Generationcited by 13
  11. 2024Synthetic Data: Revisiting the Privacy-Utility Trade-offcited by 7
  12. 2024Multi-objective Deep Learning: Taxonomy and Survey of the State of the Artcited by 6
  13. 2024Scorecards for Synthetic Medical Data Evaluation and Reportingcited by 4
  14. 2024Architectural Selection Framework for Synthetic Network Traffic: Quantifying the Fidelity-Utility Trade-offcited by 4

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