Real-world evidence (RWE)
Real-world evidence (RWE) is the clinical evidence about the usage and potential benefits or risks of a medical product derived from the analysis of real-world data. Whereas RWD describes the raw inputs (records, claims, registries, device data), RWE is the output of a structured study design and analysis applied to them: a comparative effectiveness study, a post-authorisation safety study, an external control arm or a burden-of-illness analysis.
Regulators have moved from cautious acceptance to structured frameworks. The FDA, under the 21st Century Cures Act, issued its RWE Framework (2018) and a series of guidances on data standards, registries and EHR-derived data, and accepts RWE for label expansions and post-marketing requirements. The EMA and the Heads of Medicines Agencies published a Big Data Steering Group roadmap, launched the DARWIN EU federated network, and use RWE in pharmacovigilance and in the assessment of medicines for rare diseases. Health technology assessment bodies under Regulation (EU) 2021/2282 increasingly rely on RWE for relative effectiveness. Methodological standards (ISPOR-ISPE good practices, ENCePP, HARPER protocol template, target-trial emulation) address confounding, bias and reproducibility.
The evidence itself is usually aggregated and therefore not personal data, but its generation is a processing operation on pseudonymised health data governed by the GDPR and national research law, with the same requirements as any secondary use: a research legal basis, Art. 89 safeguards, a DPIA, clear allocation of controller roles between data holders, sponsors and analytics vendors, and transfer mechanisms where analyses are performed outside the EEA. Federated approaches, in which analysis code travels to the data rather than the reverse, are increasingly used to reduce these burdens.
No. Dataset size alone does not establish whether an analysis can answer the clinical question. RWE depends on suitable data and an appropriate study design and analysis. Define the question first, then assess data relevance, completeness, missing information and potential bias before treating the results as evidence for a decision.
