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

2025-05-08

AI Summary

This study addresses the privacy–utility trade-off in clinical trial data synthesis and the unclear effectiveness of hyperparameter optimization (HPO) strategies. We systematically evaluate how HPO impacts the synthetic fidelity of eight generative models—including TVAE, CTGAN, and CTAB-GAN+—and integrate clinical domain constraints (e.g., survival analysis) to enhance credibility. We propose a “HPO + domain knowledge” co-design paradigm: (1) multi-objective metric optimization is shown to significantly outperform single-metric optimization; (2) HPO must be tightly coupled with preprocessing and postprocessing to satisfy clinical constraints, reducing constraint violation rates from 61% to an acceptable level; and (3) synthetic data achieve up to 60% higher utility in downstream clinical tasks, with simultaneous improvements in generalizability and regulatory compliance.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Optimization
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance

Abstract

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) has been shown to improve generative model performance, the effectiveness of different optimization strategies for synthetic clinical data remains unclear. This study systematically evaluates four HPO strategies across eight generative models, comparing single-metric optimization against compound metric optimization approaches. Our results demonstrate that HPO consistently improves synthetic data quality, with TVAE, CTGAN, and CTAB-GAN+ achieving improvements of up to 60%, 39%, and 38%, respectively. Compound metric optimization outperformed single-metric strategies, producing more balanced and generalizable synthetic datasets. Interestingly, HPO alone is insufficient to ensure clinically valid synthetic data, as all models exhibited violations of fundamental 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 create high quality synthetic datasets. Our study provides actionable recommendations for improving synthetic data generation, with future research needed to refine metric selection and validate these findings on larger datasets to enhance clinical applicability.

Problem

• Ensuring synthetic clinical data fidelity and utility
• Evaluating hyperparameter optimization strategies for generative models
• Addressing domain constraint violations in synthetic datasets

Innovation

• Hyperparameter optimization improves synthetic data quality
• Compound metric optimization outperforms single-metric strategies
• Domain constraints integration ensures clinically valid data