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Real-World Evidence to Inform Regulatory Decision Making: A Scoping Review.

Marieke S JansenOlaf M DekkersSaskia Le CessieLotty HooftHelga GardarsdottirAnthonius de BoerRolf H H Groenwold
Published in: Clinical pharmacology and therapeutics (2024)
Real-world evidence (RWE) is increasingly considered in regulatory decision making. When, and to which extent, RWE is considered relevant by regulators likely depends on many factors. This review aimed to identify factors that make RWE necessary or desirable to inform regulatory decision making. A scoping review was conducted using literature databases (PubMed, Embase, Emcare, Web of Science, and Cochrane Library) and websites of regulatory agencies, health technology assessment agencies, research institutes, and professional organizations involved with RWE. Articles were included if: (1) they discussed factors or contexts that impact whether RWE could be necessary or desirable in regulatory decision making; (2) focused on pharmacological or biological interventions in humans; and (3) considered decision making in Europe or North America, or without a focus on a specific region. We included 118 articles in the scoping review. Two major themes and six subthemes were identified. The first theme concerns questions addressable with RWE, with subthemes epidemiology and benefit-risk assessment. The second theme concerns contextual factors, with subthemes feasibility, ethical considerations, limitations of available evidence, and disease and treatment-specific aspects. Collectively, these themes encompassed 43 factors influencing the need for RWE in regulatory decisions. Although single factors may not make RWE fully necessary, their cumulative influence could make RWE essential and pivotal in regulatory decision making. This overview contributes to ongoing discussions emphasizing the nuanced interplay of factors influencing the necessity or desirability of RWE to inform regulatory decision making.
Keyphrases
  • decision making
  • transcription factor
  • risk assessment
  • healthcare
  • public health
  • systematic review
  • heavy metals
  • health information
  • big data
  • deep learning