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

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.