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Regulatory and HTA Considerations for Development of Real-World Data Derived External Controls.

Lesley H CurtisOriol Solà-MoralesJulien HeidtPatrick Saunders-HastingsLaura WalshDeborah CassoSusan OliveriaTiffany MercadoRobbert ZusterzeelRachel E SobelJessica J JalbertVera MasteyJames HarnettRuben G W Quek
Published in: Clinical pharmacology and therapeutics (2023)
Regulators and Health Technology Assessment (HTA) bodies are increasingly familiar with, and publishing guidance on, external controls derived from real-world data (RWD) to generate real-world evidence (RWE). We recently conducted a systematic literature review (SLR) evaluating publicly available information on the use of RWD-derived external controls to contextualize outcomes from uncontrolled trials submitted to the European Medicines Agency (EMA), Food and Drug Administration (FDA), and/or select HTA bodies. The review identified several key operational and methodological aspects for which more detailed guidance and alignment within and between regulatory agencies and HTA bodies is necessary. This paper builds on the SLR findings by delineating a set of key takeaways for the responsible generation of fit-for-purpose RWE. Practical methodological and operational guidelines for designing, conducting, and reporting RWD-derived external control studies are explored and discussed. These considerations include: (1) early engagement with regulators and HTA bodies during the study planning phase; (2) consideration of the appropriateness and comparability of external controls across multiple dimensions, including eligibility criteria, temporality, population representation, and clinical evaluation; (3) ensuring adequate sample sizes, including hypothesis testing considerations; (4) implementation of a clear and transparent strategy for assessing and addressing data quality, including data missingness across trials and RWD; (5) selection of comparable and meaningful endpoints that are operationalized and analyzed using appropriate analytic methods; and (6) conduct of sensitivity analyses to assess the robustness of findings in the context of uncertainty and sources of potential bias.
Keyphrases
  • electronic health record
  • transcription factor
  • big data
  • drug administration
  • clinical evaluation
  • healthcare
  • primary care
  • public health
  • machine learning
  • social media
  • clinical practice
  • adverse drug