Clear Criteria for Assessing Regulatory-Grade Real-World Data Sources
The discussion at the ISPOR Annual Meeting focused on defining regulatory-grade real-world data (RWD) sources as those of adequate quality for specific regulatory purposes. Panelists emphasized the importance of authenticity, transparency, accuracy, and track record in evaluating data quality. They also highlighted the significance of data relevance in terms of sample size, representation, and follow-up duration for assessing treatment effects. These criteria aim to ensure that RWD sources provide credible real-world evidence to inform regulatory decisions effectively.
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Presentation Transcript
Are there clear criteria for Are there clear criteria for whether real whether real- -world data sources world data sources are regulatory are regulatory- -grade? grade? Issue Panel ISPOR Annual Meeting May 2023 Marc L. Berger, MD Moderator
Panelists Jacqueline Corrigan-Curay, JD, MD, Director, Principal Deputy Center Director, Center for Drug Evaluation and Research, US Food and Drug Administration Jesper Kjaer, Director of The Data Analytics Centre at the Danish Medicines Agency and co-chair of HMA / EMA Big Data Steering Group William Crown, PhD, Distinguished Research Scientist, The Heller School at Brandeis University
Framing of the Issue Framing of the Issue One proposed definition of Regulatory Grade Real-World- Data (RWD) Sources Adequate quality for the intended purpose (ex. answer a question posed by a regulatory body, label changes) Fit-for-Purpose (adequate quality and has data elements required to support a rigorous protocol and analysis that can produce credible Real-World-Evidence (RWE) to inform regulatory decision at hand
Measures of Data Quality Authenticity: Provenance is well-documented and can be verified Transparency: Adequate disclosure of ETL procedures, data curation (ex. handling of missing or inaccurate data, etc.), data linkages, etc. Accuracy: Data adequately describes underlying concepts Summary of results of data checks for completeness, plausibility, conformance to CDM, uniqueness of patient records, continuity of data collection, etc. Track Record: historical performance of data source in supporting the creation of credible RWE (published and unpublished)
Fit-for-Purpose Adequate Quality + Relevance Relevance Adequate sample size of population of interest Representative of the population intended for use Adequate length of follow-up to assess treatment effects
