A Quick Glimpse into RWD and RWE
By Wanjiang Wang on July 1st, 2023
What is RWD?
Real-world data (RWD) encompasses a broad spectrum of information gathered from various sources, making it challenging to define thoroughly (Makady et al.). The definition adopted by FDA describes RWD as “data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources”. I think the key distinguishing feature of RWD lies in its origin, as it is not collected for a specific purpose such as a randomized controlled trial (RCT) or an intervention. Typically, RWD usually includes electronic health records (EHRs), product and disease registries, claims and billing data, data collected from personal health devices under non-research settings.
Why RWD is important?
Just like its name suggests, Real-World Data (RWD) is acquired from actual real-world settings, providing valuable insights into how drugs perform in natural environments instead of controlled settings. As mentioned earlier, RWD is routinely collected, making it an incredibly rich resource compared to data gathered from randomized controlled trials (RCTs), as it contains a broader range of patient information, including longitudinal medical histories. Moreover, RWD holds significant potential in enhancing the clinical development process by serving as external control data in clinical trials, thereby enhancing the external validity of RCTs.
What is RWE?
As defined by the FDA, real-world evidence (RWE) contains clinical evidence derived from the analysis of real-world data (RWD), providing valuable insights into the usage, potential benefits, and risks associated with a medical product. Unlike randomized controlled trials (RCTs) that often have stringent inclusion and exclusion criteria, certain populations, such as the elderly (65+), pregnant women, and racial minorities, who are representative of real-world scenarios, tend to be excluded from these trials (Wang et al.). Consequently, it becomes challenging to generalize the safety and efficacy of a drug to these populations. However, RWE serves as a valuable tool to address this limitation by offering scientific evidence generated from the analysis of RWD.
Challenges of RWD and RWE
Despite the numerous advantages highlighted earlier regarding RWD and RWE, it is important to acknowledge the challenges they encounter. Foremost, one significant issue with RWD lies in data quality. Given that RWD is not specifically collected for research purposes, there is an increased likelihood of encountering errors and a higher incidence of missing data, which compromises its reliability. Furthermore, since RWD is secondary data, the meanings of collected variables can sometimes be unclear, and crucial information required for research may be absent. Additionally, utilizing RWD from various sources poses challenges, as different groups may assign different variable names to identical content, making data integration and cleansing arduous tasks. Although the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) has made substantial progress in standardizing vocabularies, numerous challenges persist. Moreover, to produce reliable clinical evidence, researchers must meticulously address confounding factors and biases to facilitate causal inference regarding the effects of treatments, drugs, or products on health outcomes, as randomization is not employed in observational studies.
Despite the hurdles, RWD and RWE hold immense promise in streamlining clinical research and observational studies. By incorporating RWD into the clinical development process, valuable resources and time can be saved. As data quality continues to improve, and advancements in statistical methods and causal inference techniques are made, researchers can harness the power of RWD and RWE to generate research findings and evidence that are repeatable, reproducible, replicable, generalizable, and reliable.
- Makady, A., et al. "What Is Real-World Data? A Review of Definitions Based on Literature and Stakeholder Interviews." Value Health 20.7 (2017): 858-65. Print.
- Wang, S. V., et al. "Using Real-World Data to Extrapolate Evidence from Randomized Controlled Trials." Clin Pharmacol Ther 105.5 (2019): 1156-63. Print.
- OHDSI. [Accessed 2023-06-17]; http://ohdsi.org/
- Framework for FDA’s Real-World Evidence Program (2018). U.S. Food & Drug Administration (FDA). Retrieved from https://www.fda.gov/media/120060/download
- Levenson, Mark, et al. "Biostatistical considerations when using RWD and RWE in clinical studies for regulatory purposes: a landscape assessment." Statistics in Biopharmaceutical Research 15.1 (2023): 3-13.
- Chen, Jie, et al. "The current landscape in biostatistics of real-world data and evidence: clinical study design and analysis." Statistics in Biopharmaceutical Research 15.1 (2023): 29-42