Some Assembly Required: Solving the Puzzle of Real-World Data/Evidence

he subject of real-world data—“big data” from electronic medical records, claims, prescription databases, and other sources—is becoming more and more real, as industry and regulators look to use real-world data as real-world evidence in clinical research and therapeutic product development. Global Forum Regional Editor for Europe Julie O’Brien (Pfizer) leads the below exploration of the current and potential future states of real-world data and real-world evidence from the perspectives of the regulated and the regulators, with Jennifer Dudinak (Bristol Myers Squibb) and Max Wegner (Bayer AG).

Julie: To set the framework for our questions, would you briefly define real-world data and real-world evidence (and the difference between the two) from the clinical research and regulatory perspectives?

Max: Real-world data is data relating to patient health status and the delivery of healthcare routinely collected from electronic health records, claims and billing activities, product disease registries, patient-generated data, and data generated from other sources like mobile devices. Real-world evidence is defined as the clinical evidence regarding the use, and potential benefits or risks, of a medicine or product derived from analysis of real-world data. This can be generated by different study designs or analyses including but not limited to randomized trials, large simple trials, pragmatic trials, and observational studies, prospective and/or retrospective. These concepts had been first defined by the US FDA. They have undergone slight variations depending on the region you are in.

To give a more general picture: Imagine you have a big puzzle with several thousand pieces in front of you. Each piece of that puzzle represents real-world data. Once you put the puzzle pieces together and create the designed images, that’s real-world evidence.

This can be used in various stages of clinical research or to define what you want to do in clinical studies later, by selecting the right patient population and better understanding it in the context of the real world. Real-world evidence complements data from randomized trials to provide additional insights into long-term safety, effectiveness, and outcomes in diverse patient populations. Regulatory professionals use real-world data to monitor post-marketing safety, understand real-world treatment patterns, and evaluate the use of medicinal products in broader populations. Agencies increasingly recognize the importance of this type of data in supporting regulatory decisions such as labeling updates, post-approval commitments, or expanded indications.

Julie: Jennifer, what are some key elements of ensuring that real-world data is fit for this purpose and how well has industry demonstrated as much to regulators?

Jennifer: This is a key area. FDA has said in public forums that real-world data “fitness for use” is one of the most common reasons that sponsor proposals are not accepted. It’s a tough area but it’s one that we’re getting better at. Key elements include data relevance, accrual, provenance, completeness, and accuracy. From an accumulation of experience, and now the extensive guidances on this topic released by FDA and other health authorities, the principles for how regulators are assessing real-world data fitness for use are becoming clearer and more established.

Starting with the research question: Casting a wide net on potential data sources and conducting feasibility analyses to drive your proposed choice of data source, and not the other way around (having the data source drive your research question) are all key. It is also critical to meet with the regulatory agency or health authority to discuss these aspects upfront and early. There are opportunities for us to do even better. Although the principles are now getting quite well established, how to actually operationalize them—meaning what specific information to put in the protocol versus other documents, what information is needed in case of FDA inspection, etc.—are things we’re still learning as we go. Initiatives like the TransCelerate Real-World Data Audit Readiness Considerations and similar initiatives are trying to help with this operationalizing piece to better define documentation, best practices, and inspection preparedness.

Max: Health authorities are creating real-world data and real-world evidence pilots that will increase interactions between industry and regulators and offer a “safe to try” space for condensed discussions around these topics.

Julie: Jennifer, would you address the importance of real-world data and real-world evidence for patient recruitment and engagement, and then for clinical research and care?

Jennifer: Real-world evidence has a history of being used to help with patient recruitment. This use is expanding rapidly and is becoming quite sophisticated in the context of patient matching for trial enrollment. The use of real-world data and associated matching algorithms can provide patients and providers a much better understanding of trials they would be best fit for and would also supercharge enrollment for drug sponsors and researchers regarding clinical research and care. The ultimate goal of much of this real-world evidence work is a learning healthcare system where data generated in routine clinical practice not only serves your individual care as a patient but can also contribute to research and our understanding of public health to improve outcomes for everyone.

Julie: Max, what about the importance of real-world data and real-world evidence for clinical research and regulatory professionals and then for health technology assessment (HTA) bodies and payers?

Max: Real-world data and real-world evidence are significantly important to all the stakeholders you just mentioned. The problem sometimes is that they look at the data quite differently. I mentioned the importance that it holds for clinical and regulatory professionals. If you look across the drug development lifecycle, real-world data and real-world evidence can be used from the early stages, from pre-clinical phases where it can help in defining the target population and understanding standard of care, disease burden, unmet medical needs—a lot of elements that can be checked very early on. In every phase, we see how real-world data and real-world evidence can help inform the trial design, supplement randomized controlled clinical trial data, monitor safety, facilitate clarity around effectiveness, and expand labels for new indications and populations.

The difference comes from the perspective with which we look at these types of data. Regulatory agencies are looking at it through the lens of safety and efficacy, while HTA bodies and payers are focusing on understanding the value the products offer in certain subpopulations, the impact on patients in accessing treatment, and the unmet medical need which the new medicine or product or label or indication will be able to address.

In Europe, we see many activities bringing together regulators, HTAs, and payers, to learn from each other and collaborate for the best outcome. The new EU HTA regulation will aim to conduct parallel joint scientific advice and consultations between regulators and health technology assessment bodies with the objective of generating optimal and robust evidence that satisfies the needs of both. As industry, we encourage engagement between various regulatory bodies. But we also advocate that the role of participants in this information exchange must be very clearly defined and ensure that HTA and other payer decision-making considerations do not interfere with the regulatory assessment procedures.

An example which aims to facilitate the understanding used by both regulators and payers is DARWIN EU, which aims to have two HTA case studies included in the overall data analysis plan by the end of the year. The end goal is to achieve harmonization across Europe on early engagement with payers and to ensure that regulators, HTAs, and payers make decisions using the same core data sets and within parallel timelines, while maintaining confidentiality.

Julie: DARWIN EU is EMA’s own federated network of real-world data sets and is the flagship project in Europe for real-world evidence. Max, how do you see DARWIN EU interface with other recent initiatives such as the European Health Data Space regulation and the EU pharmaceutical legislation review?

Max: It’s fair to say that DARWIN EU will connect the European Medicines regulatory network to the European Commission’s European Health Data Space (EHDS), an initiative to promote better exchange and access to different types of health data. Personally, I’m very excited about this first-of-its-kind European Health Data Space Regulation, which was released last year for public consultation. This promises to advance the responsible exchange of health data across Europe, to put people in control of their own health data in their country and across borders, and to improve the use of health data for research, innovation, and policy-making. DARWIN EU will support real-world data analysis pilots throughout the EHDS system starting this year and will aim to produce a first report by the end of next year (2024). It’s important to keep an eye on DARWIN EU, as it will offer the baseline for EHDS data quality and labeling work.

Regarding EU Pharmaceutical Legislation Package review, the time is now to make a long-lasting change. As you’re aware, the European Commission has released a revised package for European pharmaceutical legislation. This process is happening after almost 20 years of work within a robust framework that offers little understanding of the use and applications of real-world data. The new proposal touches on various elements related to the increased focus of integration of evidence generation within product development, and its use of real-world evidence in regulatory decision-making is quite positive. These legislative proposals explicitly state that regulatory decision-making on the development, authorization, and supervision of medicines may be supported by access and analysis of health data, including real-world data. However, the legislation increases the requirements for submission of raw data as part of regulatory submissions to EMA. From an industry perspective, our main concern is how EMA’s transparency policy will be applied to any data submitted. Additionally, there are concerns regarding exclusion of marketing authorization holders from decisions related to labeling changes when additional data sources will be made available to CHMP. In conclusion, collaboration is key between regulators, industry, and healthcare stakeholders. That is crucial to establishing a robust methodology and standards for generating and evaluating real-world evidence. We need to ensure that industry regularly engages with regulators and policymakers and offers constructive feedback as we partner to provide the best outcomes for further enhancing patient access to safe and effective medicines.

Julie: ICH recently released a reflection paper on real-world evidence terminology and general principles with a view to ultimately developing an ICH guidance that would further enable integration of real-world evidence into regulatory submissions and decision-making. Jennifer, would you explain the role of regulators globally in advancing this framework, and different approaches being taken in one or two countries outside of the US or Europe?

Jennifer: Regulators have been moving real-world evidence activities forward at an exceptional pace. Numerous countries have issued multiple guidance documents on this topic, and as a result harmonization has become critical. The ICH work and roadmap outlined in this reflection paper are key to advancing this harmonization. One interesting approach we have seen in Canada highlights the importance of not only regulatory harmonization across regions, but also harmonization between regulators and HTA bodies. Here, Canada’s Drug and Health Technology Agency (CADTH) collaborated with Health Canada to jointly release a guidance on reporting real-world evidence results for both HTA and regulatory purposes. This is a great example of partnership between a regulator and HTA.

Max: I had the pleasure to chair the session on this topic at the DIA Global Annual Meeting this year with Jennifer and representatives from the FDA, EMA, and Duke-Margolis (Time for Alignment? A Policy Landscape Update on the Use and Acceptance of Real-World Data/Real-World Evidence for Regulatory Purposes). Through these discussions, we will be able to enhance engagement with health authorities and ensure that real-world evidence will go beyond complementing randomized clinical trials, as some members of at least some agencies have indicated. We need to be bold. We need to be open to new opportunities. And we need to carry the torch of what the future of drug development could look like.

Julie: With that in mind, what is the best approach to, or what is your wish list for, reaching global harmonization on the acceptance of real-world evidence for regulatory decision-making?

Jennifer: One of the most important areas in global harmonization goes back to what we talked about at the top of this discussion: data fitness for evaluation. This is such a key success factor for real-world evidence proposals; having consistent principles and ways of working for where certain information should be provided in which documents across regions will be key.

Max: I remember the discussion we had at DIA on prioritization: What is important for harmonization efforts and what is key for success here? I see three priorities. The first is international acceptance and recognition. We can’t let things go country by country. It would be important to have a harmonized effort through ICH. DIA may also play a role here, certainly also industry associations and agencies collaborating among each other. Second, data quality and standardization: What is the level of data quality needed to have a robust real-world data package, to change what we normally can only achieve through randomized clinical trials? The third is patient-centricity and stakeholder involvement. We need to make sure that we incorporate the patient perspective in real-world evidence generation, as this is vital for regulatory decision-making.

Julie: Jennifer, what needs to happen to make real-world data and real-world evidence more useful to professionals in clinical research and clinical care in the future?

Jennifer: A central point is aligning the incentives. This is really powerful because we need to have patients actually want their data to be used in research. We also need healthcare professionals to want to input the information reliably and comprehensively. We need both in order to experience the insights that real-world evidence and data will bring to them. These aren’t easy things to do, but we need to keep making headway together.

Julie: Max, what must happen to make real-world data and real-world evidence more useful in therapeutic product development and regulation and then ultimately in marketed and reimbursed use?

Max: Standardization in data quality, quality assurance of the data that we are creating and generating. Integration with clinical trials and patient-centric approaches. And in the end, most importantly, regulators’ and HTA payers’ acceptance and recognition of the type of data that we want to bring forward. We have to bring to the table better information on patient needs and where drugs can provide the most benefit.