Real world data (RWD) is transforming clinical development and it is critical to understand how RWD and randomized clinical trials (RCTs) can work together to bring life saving medicines to patients faster.
Across the healthcare industry, data is created, collected, and curated for a variety of purposes. From clinical codes and medical claims to quality assurance data and patient experience surveys, each of these data types uses a different lens to help develop holistic insights into the broader patient care ecosystem.
The challenge for researchers is to bring these disparate data sources together to solve specific problems, such as improving health equity, reducing unnecessary utilization, or developing a brand-new therapy for oncology patients.
Some of these data sources are better suited for specific challenges than others due to their quality, completeness, and accuracy. For example, randomized controlled trials (RCTs) are highly desirable due to their rigorous methodology for capturing relevant data elements and carefully controlling for variables.
However, this high-quality data comes at a cost. Clinical trials are often expensive and time-consuming, and they tend to only include small and specific populations, potentially restricting their applicability to broader groups.
On the other end of the spectrum is the vast and varied universe of real-world data (RWD). RWD includes just about every data type collected during patient care, including electronic health record (EHR) data, claims, product and disease registries, and patient-generated data from surveys, devices, and other sources.
RWD has the potential to paint a much more complete and more longitudinal portrait of how a therapy or protocol functions in the wild, but there’s a price to pay for this knowledge, too. RWD is complex and often “messy,” since each component was collected in a different environment for a different purpose. RWD may require careful selection and additional curation to be truly useful.
Fortunately, we don’t have to choose between one or the other. Both RCTs and RWD bring significant value to researchers and can complement each other in innovative and exciting ways.
How RCTs and RWD can work together to produce better research
RCTs are often considered the gold standard of clinical research, and for good reason. By randomizing patients into intervention and control groups and limiting unwanted variables, RCTs are a vital foundation for exploring the safety and efficacy of promising therapies.
But there’s much more to patient outcomes than what happens within the tightly controlled walls of the clinic. And that’s where RWD can step in.
Real-world data can augment and enhance the critical work of RCTs, filling in gaps and expanding the research community’s ability to make fully informed decisions about the next steps for their product or care process.
Here are some of the top ways RCTs and RWD can work together to make oncology research even more complete, actionable, and informative.
Combining the strengths of RCTs and RWD can make both types of data more useful, particularly for rare diseases and hard-to-treat cancers that urgently require new avenues of treatment.
Envisioning the future role of multi-source data in research and development
RWD and RCTs are already playing an important part in furthering the research, development, and ongoing surveillance of cancer therapies, but the life science industry is just beginning to unlock the true potential of these rich and varied data sources.
In this new blog series from COTA and Deloitte, we will explore the expanding role of RWD in the oncology environment, including how to integrate RWD into research and regulatory submission to the FDA.
Next time, we’ll take a look at what makes a fit-for-purpose (FFP) database and how to leverage different types of data to answer pressing questions in oncology research.