Real-world data (RWD) has become a buzzword phrase in recent articles in clinical trial research. But there is often a difference between the idealistic and future use of RWD and how it can and is practically being used to advance drug development. What is RWD? How is RWD being applied now? What are the potential uses for it in the future? 

Per the FDA, RWD are data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources.  Examples of RWD include data derived from electronic health records, medical claims data, data from product or disease registries, and data gathered from other sources (such as digital health technologies) that can inform on health status.  Real-world evidence (RWE) is the clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD.[1] These concepts are not new. Regulatory agencies like the FDA have for years used RWD and RWE to monitor post-marketing safety of approved drugs. The new development is the rising use of RWD by pharmaceutical companies to support drug development not only by generating RWE but as a tool to support decision making even in the setting of randomized clinical trials (RCTs). 

The use of RWE has increased over time. According to GlobalData’s Clinical Trials Database, 2021 had the highest annual amount of real-world evidence trials. As of November 2024, the number of RWE trials was >300 with oncology being a top therapeutic area for its use.[2] In 2022, the FDA announced an Advancing RWE Program to fulfill a commitment under the Prescription Drug User Fee Act (PDUFA) VII for fiscal years 2023 through 2027.  The program includes a new mechanism for identifying approaches to generate RWE based on RWD that meet regulatory requirements in support of labeling for effectiveness. 

Less documented are the other benefits of RWD in drug development. RWD can be used to inform decision making by better identifying populations that will respond to treatment, speeding up development by providing a more comprehensive review of patients which can inform exclusion criteria and patient recruitment, and helping companies assess the economic impact of a new drug. As noted by the Heads of Medicines Agencies of the European Medicines Agency (HMA-EMA), an organization that co-operates with the EMA and European Commission  in the operation of the European medicines regulatory network, “While randomized, double-blind controlled trials will remain the reference standard for most regulatory-use cases, the complementary evidence that big data sources generate may facilitate, inform, and improve decisions”. 

RWD is increasingly impacting the way drugs are developed. The impact goes beyond using RWD solely to generate RWE to support submissions. By using RWD to complement data from traditional clinical trials to inform clinical planning and trial design, drug developers can more efficiently make decisions that will accelerate the development of drugs that are safer, more effective, and fit for use for appropriate patient populations even when using more traditional pathways of drug development. As you prepare to design your next study and grapple with questions on RWD and RWE, contact us to speak with one of our statistical experts.

[1] Real-World Evidence | FDA 

[2] Real-world evidence trials set to increase for 2024 – Clinical Trials Arena 

Patricia Stephenson, Sc.D., Associate Director, Biostatistics is a Harvard graduate with over 10 years of experience working in oncology, including working with researchers at the Dana-Farber Cancer Institute. Dr. Stephenson has served as the lead statistician for multiple oncology studies, including a Phase 1 study pivotal for an NDA submission for accelerated approval. As a result, she was involved in several submission activities including supporting the statistical preparation and review for the Summary of Clinical Efficacy (SCE) and Summary of Clinical Safety (SCS) as well as preparation for a FDA Advisory Committee Meeting.  Previously, Dr. Stephenson also served as the lead statistician for multiple Phase 1 and 2 studies in ovarian cancer, renal cell carcinoma, gastrointestinal stromal tumors, and non-small cell lung cancer.