In recent years, Real-World Data (RWD) and Real-World Evidence (RWE) have taken on a strategic role in regulatory decision-making. If you work in clinical research or regulatory affairs, you are probably already seeing it: agencies are no longer relying solely on randomized trials to evaluate treatments.
Institutions such as the U.S. Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Haute Autorité de Santé (HAS) have published several methodological frameworks to guide the use of real-world data.
But in practice, how far can these data go? Under what conditions are they truly accepted by regulatory authorities?
RWD and RWE: two concepts that must be clearly distinguished
According to the U.S. Food and Drug Administration, Real-World Data (RWD) refers to data relating to patient health status and/or the delivery of healthcare that are routinely collected from a variety of sources (electronic health records, patient registries, reimbursement claims, as well as health apps and connected devices). In short, anything that reflects patients’ real-life experiences.
Real-World Evidence (RWE) refers to the clinical evidence regarding the usage, effectiveness, or safety of a medical product derived from the analysis of these data.
In other words, RWD are the raw material, and RWE are the conclusions drawn from them to inform decision-making.
What makes these data particularly valuable is that they allow treatments to be observed in much broader and more diverse populations than those typically included in traditional clinical trials.
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Why are authorities really interested in RWE?
Randomized clinical trials remain the gold standard. However, they are conducted in highly controlled environments. In real clinical practice, patients have comorbidities, receive multiple treatments, and adherence may vary depending on healthcare contexts.
RWE therefore helps answer questions that clinical trials do not always address.
In this context, the U.S. Food and Drug Administration developed its RWE Program to explore how Real-World Evidence can support label expansions for already approved drugs, fulfill post-authorization study requirements, and strengthen safety evaluations.
Similarly, the European Medicines Agency launched the DARWIN EU® network (Data Analysis and Real-World Interrogation Network), an initiative dedicated to analyzing real-world healthcare data from multiple European databases. Its objective is to rapidly generate analyses that support drug evaluation, pharmacovigilance activities, and broader regulatory decision-making across Europe.
At the national level, several initiatives also structure access to health data. In France, the Health Data Hub facilitates access to and analysis of large health datasets, particularly those from the Système National des Données de Santé (SNDS), one of the largest medico-administrative databases in the world.
These infrastructures enable large-scale real-world studies and help strengthen the generation of Real-World Evidence useful for research and health policy evaluation.
How RWE are already influencing regulatory decisions
Today, RWE are used to:
- confirm effectiveness or safety after Marketing Authorization (MA)
- support label extensions
- document populations poorly represented in clinical trials
- contribute to benefit–risk reassessments
- support certain reimbursement decisions
A frequently cited example concerns HR+/HER2- advanced breast cancer. In this context, real-world data have been used to document the use of CDK4/6 inhibitors in routine clinical practice and to complement the results of randomized clinical trials.
After the initial approval of these treatments based on phase III trials, real-world analyses made it possible to evaluate their use in routine care, particularly among older patients or those receiving multiple medications, and to better characterize their safety profile in populations often under-represented in clinical trials.
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A warning: this is not “easy data”
Real-World Evidence does not replace randomized clinical trials; it complements them.
Randomized trials remain the reference for establishing strong causal relationships thanks to randomization and bias control.
RWE instead provides a broader and more realistic view of clinical practice, including more heterogeneous populations and diverse healthcare contexts.
However, this richness comes with methodological challenges. Real-world data are often:
- heterogeneous across countries and healthcare systems
- incomplete or imperfectly coded
- exposed to biases that are more difficult to control
- less suitable for establishing direct causality
For this reason, an RWE-based study can only be considered robust if its methodology is rigorous.
Regulatory authorities closely examine:
- data quality and traceability
- bias management (selection bias, confounding bias, immortal time bias, etc.)
- statistical methods used (propensity scores, sensitivity analyses, adjusted models)
- pre-specification of analytical protocols
Recommendations from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR), as well as EMA and FDA guidelines, frame these methodological requirements.
In other words, real-world research does not eliminate the need for scientific rigor.
This evolution is also reflected in the recent work of International Council for Harmonisation (ICH) within ICH E6(R3). Its Annex 2, currently being finalized, addresses the integration of new data sources and more pragmatic study designs, including decentralized trials and other innovative approaches to data collection.
A future where RWE become unavoidable
With the rise of digitalization, richer databases, and artificial intelligence, RWE are gaining importance.
Regulatory authorities are developing new guidelines, and pharmaceutical companies are investing in well-structured registries.
All signs indicate that RWE will soon become a central pillar of the regulatory process.
And for professionals working in clinical research or regulatory affairs, this means preparing for a future where these data will increasingly play a role in projects.
At the European level, this transformation is also linked to the development of the European Health Data Space (EHDS), the future European health data space.
This regulation aims to facilitate the secondary use of health data for research, innovation, and public policy evaluation. Its progressive implementation should eventually enable more harmonized use of health data across Europe.
Conclusion
Real-World Data and Real-World Evidence are gradually transforming how regulatory decisions are made.
They provide a more realistic, comprehensive, and sometimes faster view of the impact of treatments in real-life conditions.
Challenges remain significant, but the trend is clear: these data are becoming a lasting part of the regulatory landscape.
In this context, new questions arise:
Could some regulatory decisions one day rely primarily — or even entirely — on Real-World Evidence?
And could these data eventually replace certain traditional clinical trials?
The coming years may provide the answer.
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