Unpacking Real-World Data Curation: Principles and Best Practices to Support Transparency and Quality
Duke-Margolis Health Policy Center
In the coming years, there will be emerging opportunities for making better use of real-world data (RWD) within the U.S. Food and Drug Administration’s (FDA’s) regulatory framework. More consistent use of RWD sources can improve the efficiency of traditional randomized controlled trials, and there may also be opportunities to leverage RWD and resultant real-world evidence (RWE) in support of supplemental approval or labeling actions based on substantial evidence of effectiveness as envisioned in 21st Century Cures and PDUFA VI.
Developing RWE that can support regulatory decisions starts with an appropriate, or “fit-for-purpose,” dataset. Ensuring fitness-for-purpose will require fundamental questions related to the quality and provenance of the RWD. Regulators will need to understand and assess the provenance of RWD that is submitted as part of an evidence package, as well as the curation and transformation steps that were applied as data moved from inputs provided by a source, to raw data output, to development of the analytic file and dataset used for analysis.
Due to the growing diversity of source data being collected by different data systems and with a variety of data curation practices, questions remain about the quality, including the accuracy and completeness, of real world data and about transformations undertaken in the data curation process. This workshop will gather organizational perspectives and explore potential best practices, where possible, of specific curation steps or techniques. The workshop will provide an opportunity for data curators to come together pre-competitively to 1) aid FDA in better understanding the types of curation characteristics that may need to be documented to support evaluation of the data during regulatory review, and 2) further strengthen the field of large-scale RWD development and curation.