Real-world data (RWD) are playing an increasingly important role in understanding how therapeutics perform in routine clinical practice. While randomized clinical trials remain the foundation for regulatory authorization, real-world evidence allows us to evaluate how therapies are used once they enter broader clinical practice. By capturing outcomes across more clinically heterogeneous patient populations, diverse care settings, and real-world patterns of dosing, adherence, and persistence, these analyses provide important context about effectiveness, safety, and how treatments are integrated into routine care.
In more common conditions, including many oncology indications, real-world evidence is already an established component of the post-authorization evidence landscape in multiple regions, including North America, Europe, and Asia-Pacific. In these settings, RWD frequently informs comparative effectiveness discussions, treatment sequencing, persistence, and healthcare utilization within increasingly complex and competitive therapeutic environments.
Rare disease presents a different dynamic.
While RWD is often applied post-authorization in common conditions, in rare diseases its role is frequently broader and more foundational. It may contribute not only after authorization, but also earlier in development, when traditional evidence generation is constrained by small patient populations and limited understanding of disease progression. In recognition of Rare Disease Month, this is an opportunity to reflect on how real-world data contributes across the rare disease lifecycle — and why training strategies must evolve alongside the evidence landscape.
In rare diseases, RWD may support multiple phases of a product’s lifecycle:
In many rare conditions, foundational knowledge of disease trajectory may be limited. Natural history research and patient identification efforts often precede therapy development, helping define disease burden, characterize heterogeneity, and inform trial feasibility. External control arms may be considered when randomization is impractical or unethical, though their interpretability depends heavily on rigorous data selection and comparability between real-world and trial populations. Similarly, real-world follow-up may help contextualize surrogate endpoints observed in trials by examining whether biomarker changes translate into meaningful long-term clinical outcomes.
In this way, RWD can influence development strategy well before regulatory review, and continue to add context after authorization.
Regulatory agencies around the world are increasingly formalizing how real-world evidence may support decision-making. In the United States, the 21st Century Cures Act directed the U.S. Food and Drug Administration to establish a framework for evaluating real-world evidence. In the European Union, the European Medicines Agency and the European Medicines Regulatory Network have advanced initiatives such as DARWIN EU to leverage routine healthcare data for regulatory assessment. Authorities in Asia-Pacific, including Japan’s Pharmaceuticals and Medical Devices Agency and China’s National Medical Products Administration, have also developed guidance to support RWD in regulatory and post-marketing contexts. International collaboration continues to shape evolving standards for RWE generation and interpretation.
Once a therapy enters real-world clinical use, additional complexity emerges. Patients treated outside pivotal trials may differ meaningfully in genotype, disease severity, comorbidities, care environment, and adherence patterns. In rare diseases, even small differences between patient subgroups can meaningfully influence outcomes. Real-world evidence therefore provides important insight into how therapies perform across clinically meaningful subsets and under routine practice conditions.
Post-authorization RWD may also help address practical questions from regulators, payers, health technology assessment bodies, guideline committees, and healthcare providers globally, including:
How does this therapy perform outside the controlled trial environment?
In high-cost rare disease settings, these data may also inform health economic evaluations and broader value and health economic discussions.
As real-world evidence becomes more visible in publications, scientific meetings, and guideline discussions, field-facing teams are increasingly likely to encounter questions that extend beyond pivotal trial data.
In rare disease settings, those questions may involve natural history findings, external controls, surrogate validation, registry analyses, or long-term observational follow-up. These evidence types serve different purposes and carry distinct methodological considerations.
Real-world evidence cannot be interpreted in isolation. Its strength depends on data source characteristics, completeness, follow-up duration, and analytic rigor. Registry datasets, claims databases, and electronic health record sources each have distinct strengths and limitations. In rare diseases, small sample sizes and evolving datasets make thoughtful interpretation especially important.
Both clinical trial data and real-world evidence continue to mature over time. Long-term extension studies and accumulating observational follow-up can refine understanding of durability, safety, and patterns of use.
Training programs therefore need to prepare teams not only to communicate an initial data package, but to interpret and contextualize an evolving evidence landscape. While RWD are not yet equally available across all rare diseases. However, their role in development, regulatory evaluation, and post-authorization assessment continues to expand globally. Organizations that proactively build real-world evidence literacy into their rare disease training strategies will be better positioned to support meaningful scientific exchange with healthcare stakeholders worldwide as the evidence landscape continues to evolve.