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Executive Summary
The IDERHA project has developed draft policy recommendations to support the acceptance and use of Real-World Data (RWD) and Real-World Evidence (RWE) in regulatory and Health Technology Assessment (HTA) decision-making for medicines and medical technologies. These recommendations aim to foster trust, transparency, and global alignment by promoting good research practices, data integrity, and governance. They are aimed at public decision-makers (regulatory authorities and HTA organisations) and researchers (industry, academia, clinical organisations) conducting studies for decision-making.
Traditional clinical trials remain essential but often lack generalisability and underrepresent diverse populations. RWE can complement trials by providing patient-centred insights, especially where randomised trials are unfeasible or unethical. Despite growing recognition of its value, concerns persist about robustness and evidentiary standards, and relevance in decision-making. The recommendations establish shared expectations for quality and conduct and focus on transparency and data provenance to demonstrate RWD quality and fitness for purpose.
IDERHA reviewed current regulatory and HTA policies, gathered expert feedback, and identified priority areas for alignment. The report introduces a generalised RWE study process map to ensure scientific rigor and guide organisations in developing or refining their policies, thereby strengthening confidence in the use of RWE for decision-making.
Key Recommendations for Conducting and Using RWE
- Define and Align Research Questions Early: Develop clear, relevant research questions aligned with regulatory and/or HTA needs.
- Engage Decision-Makers Proactively: Maintain dialogue with regulatory and HTA bodies throughout the study lifecycle.
- Design Fit-for-Purpose Studies: Tailor study design, data, and analyses to decision-making contexts.
- Adopt Established Methodological Frameworks: Use recognised guidelines (e.g. EMA, FDA, NICE, ICH) and register study protocols when appropriate.
- Ensure Data Quality and Relevance: Use reliable, relevant data sources with clear documentation of limitations and mitigation strategies.
- Promote Transparency and Documentation: Maintain comprehensive documentation of design decisions, deviations, analyses, and share results including uncertainty assessments.