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At EvidentIQ, we are all about data, and it is our mission to make it as easy as possible to collect robust, high-quality data during clinical trials, but also in registries and real-world/late phase studies.
We recognize the growing role that real-world data (RWD) and real-world evidence (RWE) are playing in decision-making processes during the conduct of clinical trials, drug development, and post-marketing. Indeed, the US Food and Drug Administration’s (FDA) announcement at the end of 2018 cited the use of RWD and RWE as a “top strategic priority” of its framework for the development of novel therapeutics.
So, it is time to take a look at the impact that RWD and RWE have had so far. Before discussing why and how RWD and RWE are being applied in decision-making (which is a topic we’ll cover in a future post), we first want to describe what is actually meant by RWD and RWE.
Definitions of RWD and RWE
According to the FDA, RWD is, “data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources”. Meanwhile RWE is, “the clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD”. What this means is that RWE is generated from RWD.
At first glance, this seems clear enough. However, upon closer scrutiny, there is actually considerable ambiguity and uncertainty in the definitions. Recently, several studies and whitepapers have sought to collate definitions used among stakeholders to develop a consensus.
For example, one study noted that most stakeholders they interviewed defined RWD as any data collected outside of the context of a randomized controlled trial. However, this description was not universal. Indeed, definitions identified by the authors differed and often contradicted one another.
Results from a roundtable discussion last year among representatives from industry, research institutions, regulatory agencies, and health technology assessment bodies echoed that finding. It was stated that “much greater precision is needed around this terminology and clearer, more accurate definitions must be established alongside guidance on using these tools”.
We are confident that the growing value of RWD and RWE in decision-making will create more consensus regarding their definitions. In the meantime, having a clear understanding of the goals and limitations of using RWD and RWE in your study will help guide the type of data collected and generated. We refer here to case studies provided in the FDA’s Framework and published recommendations by various institutions (e.g., from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the International Society for Pharmacoepidemiology (ISPE), among others).
Data Sources
In this highly digital age, sources of RWD are varied and large. In one survey, biopharmaceutical companies and contract research organizations used the following sources to support a new drug application
- Claims data (used by 95% of survey respondents)
- Electronic health records (71%)
- Prescription data (67%)
- Patient-reported outcomes (48%)
- Demographic data (48%)
According to the FDA, further sources of RWD include data from product and disease registries, patient-generated data (including from in-home-use settings) and data gathered from other devices that can inform on health status, such as mobile devices/wearables.
Conclusion
The publication of the Framework for the FDA’s Real-World Program underscores the acceptance by regulatory authorities of the value and power of RWD and RWE in validating the efficacy and benefit of drugs beyond the pivotal clinical trial.
Although a lot of work still needs to be done regarding standardizing terminology and how data is recorded, collated and analyzed, RWD and RWE hold vast potential to improve healthcare decision-making.
In the next post, we will discuss the reasons why RWE is growing in importance in other sectors and provide some case studies explaining how RWD and RWE are used with respect to drug development and beyond.
Image Credit: Ekaphon maneechot / Shutterstock.com
