How Good Research Data Management Enables Reproducibility and Data Reuse: A Case Study of Clinical Trial Data

Reproducibility and data reuse are essential for scientific transparency and maximizing the value of clinical trial data, yet they remain under-practiced. Good research data management (RDM) is central to enabling these practices by ensuring datasets are well-documented, accessible, and interoperable.

This presentation illustrates these benefits through a case study of the CAO/ARO/AIO-04 rectal cancer clinical trial. As part of the SHARE-CTD doctoral program, a multi-team datathon was organized to independently reproduce the trial’s primary findings using anonymized patient-level data and supporting materials. Five teams employed R and Python to replicate key outcomes, tables, figures, and statistical analyses. Despite minor discrepancies due to incomplete documentation or software differences, all teams successfully reproduced the primary results, confirming the reliability of the original findings. The datathon also enabled robustness checks and additional analyses, demonstrating how well-managed data supports secondary research beyond reproducibility. Challenges—such as broken protocol links, lack of complete data dictionaries, and partially documented analytical decisions—highlight the importance of comprehensive RDM for enabling data reuse.

This case study shows that effective RDM underpins reproducibility, transparency, and scientific trust. By providing structured metadata, code, and documentation, clinical trial data can be reused to validate results, explore new questions, and inform future research and clinical decision-making.

Researchers at UZH and other institutions