Generating Reliable Synthetic Clinical Trial Data: The Role of Hyperparameter Optimization and Domain Constraints
This work addresses data privacy and accessibility issues in medical research by improving synthetic data generation, though it is incremental as it builds on existing HPO and generative models.
The study tackled the challenge of generating high-fidelity synthetic clinical trial data by evaluating hyperparameter optimization strategies, finding that compound metric optimization improved data quality by up to 60% for some models, but HPO alone was insufficient to ensure clinical validity, with up to 61% of cases producing invalid data without proper processing.
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.