Abstract
As efforts increase to assess regulatory acceptability of real-world data (RWD) submissions, the number of guidelines around RWD quality considerations have grown. Globally, these align on emphasizing the need for transparency in RWD quality assessments, focused on the “what” and giving opportunity for “how”. The Kahn data quality framework harmonizes assessment methods across standards, defining three assessment categories (conformance, completeness, plausibility) within two contexts (verification, validation). Creation of standardized tools can operationalize RWD guidance in a transparent, reproducible manner. Objectives: (1) Translate Kahn framework into actionable business rules and thresholds defined in accordance with regulatory guidance (2) Deploy metrics through interactive OMOP-compatible dashboard, enabling determination of data’s fit-for-purpose for generation of real-world evidence.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
