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Real-World Data Analytics Fit for Regulatory Decision-Making.

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American Journal of Law & Medicine
|August 15, 2018
PubMed
Summary
This summary is machine-generated.

Healthcare database analyses provide crucial real-world data (RWD) to supplement clinical trials for medical product evaluation. These analyses support regulatory decision-making by offering meaningful, valid, expedited, and transparent evidence on effectiveness, safety, and value in routine care.

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Area of Science:

  • Health Services Research
  • Regulatory Science
  • Real-World Evidence

Background:

  • Regulatory initiatives like the 21st Century Cures Act and PDUFA recognize healthcare database analyses as valuable supplements to randomized clinical trials.
  • Real-world data (RWD) from electronic health records and claims analyses reflect medical product impact in routine care settings, distinct from controlled trials.
  • The increasing demand for evidence generation in regulatory decision-making necessitates the use of RWD for evaluating medical products.

Purpose of the Study:

  • To outline the utility of healthcare database analyses for generating evidence on medical product effectiveness, harm, and value in real-world settings.
  • To describe key regulatory evidentiary needs, including monitoring medication performance, identifying patient strata, and tracking utilization.
  • To introduce the Meaningful, Valid, Expedited, and Transparent (MVET) framework for enabling successful regulatory decision-making using healthcare database analyses.

Main Methods:

  • Analysis of healthcare databases (claims, electronic health records) to generate real-world data (RWD).
  • Application of RWD for regulatory purposes such as accelerated drug approval and secondary indications.
  • Utilizing rapid-cycle analytics for incremental evidence development synchronized with regulatory decision-making processes.

Main Results:

  • Healthcare database analyses provide essential evidence for regulatory decision-making, complementing traditional clinical trial data.
  • The MVET framework (Meaningful, Valid, Expedited, Transparent) establishes requirements for robust and trustworthy evidence generation from RWD.
  • Maturing rapid-cycle analytics of healthcare databases align with evolving regulatory demands for timely and relevant evidence.

Conclusions:

  • Evidence generation systems satisfying MVET requirements enhance regulatory decision-making effectiveness.
  • The utility of real-world data analyses is increasingly demonstrated across multiple contexts, driving regulatory acceptance.
  • Continued advancements in governance, regulations, and data quality are crucial for maximizing the potential of healthcare database analyses in regulatory science.