Leveraging Synthetic Clinical Data for Validation and Operational Readiness in Clinical Trials

Abstract

Background/Objectives: Obtaining timely access to detailed clinical trial data is not always straightforward. Privacy requirements, governance processes, and study-specific eCRF configurations can delay access, particularly during study start-up, when teams need realistic data to develop and test validation rules, reporting pipelines, and centralized monitoring tools. Methods: We developed SYNDATA, a modular framework that generates synthetic clinical trial datasets conforming to a target electronic case report form (eCRF). The framework combines study metadata from the Medidata Rave Architect Loader Spreadsheet (ALS) with selected empirical patterns learned from a closely matched reference study. It constructs patient-specific timelines from the ALS visit matrix, generates module-specific records using Bayesian networks for selected categorical dependencies and density-based methods for numeric and temporal variables, and applies postprocessing for counters, dictionary coding, and conditional missingness. A configurable Noise Tool injects controlled and reproducible data defects, including timeline inconsistencies, visit-window violations, numeric threshold exceedances, randomization or eligibility conflicts, and structural collisions, to stress-test downstream validation logic. Results: Synthetic and source data were compared descriptively using Jensen–Shannon distance, Cramér’s V, and representative plots across selected domains. Several binary operational fields showed close descriptive agreement, whereas agreement was weaker for some more complex, multi-category safety- and medication-related variables. The evaluation covered a representative subset and does not establish uniform fidelity, formal statistical equivalence, or clinical validity across all generated domains. Conclusions: SYNDATA supports reproducible generation of ALS/eCRF-conformant datasets for operational validation, reporting development, and workflow testing before real trial data are available. Its demonstrated value is limited to the evaluated operational use case; fidelity is variable-specific, and more complex domains require further development and validation.