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Real-World Data Analytics Fit for Regulatory Decision-Making

Published online by Cambridge University Press:  06 January 2021

Sebastian Schneeweiss  and
Robert J. Glynn
Sebastian Schneeweiss
Affiliation:
Division of Pharmacoepidemiology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
Robert J. Glynn
Affiliation:
Division of Pharmacoepidemiology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
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Abstract

Healthcare database analyses (claims, electronic health records) have been identified by various regulatory initiatives, including the 21st Century Cures Act and Prescription Drug User Fee Act (“PDUFA”), as useful supplements to randomized clinical trials to generate evidence on the effectiveness, harm, and value of medical products in routine care. Specific applications include accelerated drug approval pathways and secondary indications for approved medical products. Such real-world data (“RWD”) analyses reflect how medical products impact health outside a highly controlled research environment. A constant stream of data from the routine operation of modern healthcare systems that can be analyzed in rapid cycles enables incremental evidence development for regulatory decision-making.

Key evidentiary needs by regulators include 1) monitoring of medication performance in routine care, including the effectiveness, safety and value; 2) identifying new patient strata in which a drug may have added value or unacceptable harms; and 3) monitoring targeted utilization. Four broad requirements have been proposed to enable successful regulatory decision-making based on healthcare database analyses (collectively, “MVET”): Meaningful evidence that provides relevant and context-informed evidence sufficient for interpretation, drawing conclusions, and making decisions; valid evidence that meets scientific and technical quality standards to allow causal interpretations; expedited evidence that provides incremental evidence that is synchronized with the decision-making process; and transparent evidence that is audible, reproducible, robust, and ultimately trusted by decision-makers.

Evidence generation systems that satisfy MVET requirements to a high degree will contribute to effective regulatory decision-making. Rapid-cycle analytics of healthcare databases is maturing at a time when regulatory overhaul increasingly demands such evidence. Governance, regulations, and data quality are catching up as the utility of this resource is demonstrated in multiple contexts.

Type
Articles
Information
American Journal of Law & Medicine , Volume 44 , Issue 2-3: Symposium - The 21st Century Cures Act: A Cure for the 21st Century? , May 2018 , pp. 197 - 217
Copyright
Copyright © American Society of Law, Medicine and Ethics and Boston University 2018

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References

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5 See Schneeweiss et al., Assessing the Comparative Effectiveness, supra note 4, at 786.

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