onversations about the use of “big data” in clinical research and care have also triggered discussions about the relationship between real-world data (RWD), real-world evidence (RWE), and randomized clinical trials (RCTs). The session Real-World Data (RWD) Management and Application in Clinical Research continued these discussions at DIA China 2020.

Key Takeaways

• To make studies of RWE data and evidence (also known as real-world studies, [RWS]) more useful, their inherent limitations must be accounted for and addressed.
• RWS can be most useful as complements to RCTs, especially when working to expand the study sample size or population.

The role of clinical research is to develop and weigh enough clinical evidence to either confirm or reject the study hypothesis. Interest in using real-world evidence to support clinical research in this role continues to grow among research, drug discovery and development, and regulatory professionals.

Limitations of RWD and RWE, such as the quality of the data and the structure of the databases housing it, are widely recognized. For example, when these data were originally collected, these collection processes were not designed for research. In addition, many hospitals actually own the databases housing the healthcare information of their patients, and the structures of these databases often differ from hospital to hospital. Local government data initiatives, and companies working with big data, have begun to connect with and read data from these institutions.

The lack of uniform global approach to these data is also reflected in the state of requirements needed to address them from a regulatory science perspective. In January 2020, NMPA issued Guidelines for Real-World Evidence to Support Drug Development and Review (Interim) among its first steps.

Many real-world databases were not constructed to support research but to aggregate large pools of similar data. As a result, confounding bias and related data quality issues are common, as are missing and incomplete data. Only skilled collection, management, and analysis will identify the true evidence in these data.

Even with these limitations, RWS and RCT research can be complementary. RCTs develop evidence on the safety and efficacy of a drug based on randomization within a limited study population. After the drug has been approved for market use, properly structured RWS can identify and examine that drug’s safety and efficacy in a larger and more diverse population, expanding upon the research begun in the RCT and providing additional safety and efficacy evidence required for the drug to be included in national insurance. Understanding the specific research target, and designing the study protocol around it, is essential for effective RWS.

But RWS can do more than complement clinical research. They can be useful in epidemiological and other studies in the fight against COVID-19. Since the epidemic, such studies have used multiple databases (“big data”) to examine, for example, the contact tracing or travel routes of identified infected patients, asking persons along that route to get a medical check; or the sales patterns of treatments for high fever, to possibly predict future outbreaks.

RWS and RCT can and should move forward together. Using real-world studies to expand hypotheses from clinical studies can help create a secure circular loop of evidence between the two.