Real-World Evidence (RWE): Global Regulatory Landscape and Innovation
Life Sciences – In Focus Panel Discussion Episode 2 Key Learnings
Real-world evidence (RWE) has been a hot topic in recent years, not only in the research and life sciences industry but also in public health. Given the challenges surrounding its ethical use and regulation, the increasing importance of AI and the sheer volume of data available, the discussion is sure to continue. Vistatec invited industry-leading experts to share their insights, thoughts, and predictions on the role of RWE going forward at our recent “Real-World Evidence: Global Regulatory Landscape and Innovation” event.
This second panel discussion in Vistatec’s “Life Sciences – In Focus” series, where we explore current trends and topics in the Life Sciences industry, commenced with a presentation by Estelle Frappé, former Director RWE MD&D at Aetion, who provided a sweeping overview of RWE in the context of the global regulatory landscape and innovation. This presentation preceded an in-depth conversation between panelists Dan Stephens, Ph.D. from Boston Scientific, Heather M. Colvin, MPP, of Johnson & Johnson, and presenter Estelle Frappé, expertly moderated by Karen Tkaczyk, Ph.D., Director of Sales – Life Sciences at Vistatec.
The event provided valuable insights into perceptions and use of RWE and surrounding challenges and regulations today and in years to come.
Presenter and Panelists
- Estelle Frappé, Former Director RWE MD&D, Aetion (presenter)
- Dan Stephens, Ph.D., Global Advocacy – Real World Evidence, Patient Science & Early Feasibility, Boston Scientific (panelist)
- Heather M. Colvin, MPP, Director MD Regulatory Affairs Evidence and Outcomes Policy, Johnson & Johnson (panelist)
- Karen Tkaczyk, Ph.D., Director of Sales – Life Sciences, Vistatec (host)
RWE: Global Regulatory Landscape and Innovation
Estelle Frappé kicked off her presentation on RWE with a quote by the FDA’s Jeff Shuren, MD, to illustrate that while RWE was not traditionally viewed as clinical evidence, these perceptions have changed in recent years:
“I wish we could get rid of the term real-world data and real-world evidence … we were an early adopter of that at the FDA, and it’s become a four-letter word. At the end of the day, we’re talking about clinical evidence.”
Key Points
Defining RWD & RWE
The following definitions, used in an FDA guidance issued in 2017, largely overlap with those used internationally:
Real World Data (RWD)
RWD can include electronic health records (HER), claims and billing data, data from product and disease registries, patient-generated data, including in-home settings, mobile device data, and more.
Real World Evidence (RWE):
Sources of Health Data
At its heart, no matter the source, health data is always patient-derived data. The patient may generate it through digital therapeutics, wearables, apps, social media, and patient registries, or it can be gathered through providers (physicians, healthcare organizations, laboratories, EHRs, medical devices) or payers.
RWD informs RWE, which is used throughout the product lifecycle for everything from regulatory decision-making to other business processes, including:
- Premarket authorization, e.g., original product, indication/label expansion, design iterations
- Postmarket, e.g., safety/effectiveness requirements as required by regulatory agencies or voluntarily in postmarket research
- Product development and research, e.g., budget impact, cost consequence, and cost-utility analysis
- Reimbursement
- Public health surveillance.
Global RWE Adoption, Regulation and Usage
Since the FDA first issued RWE guidance to support regulatory decision-making in medical devices in 2017, other countries have followed suit. Today, RWE is increasingly recognized as clinical evidence throughout the industry and across the globe.
RWE is helpful in various aspects of the product lifecycle. Five areas stand out: Clinical trial optimization (e.g., site/patient selection, protocol adjustment, comparator arms), epidemiology (monitoring of pathology evolution/therapeutic strategies including determining the burden of disease, identifying unmet needs, and off-label use), product safety and risk management and HEOR market access as well as commercial analytics.
In addition, RWE helps meet the needs of multiple stakeholders:
Regulators
- Detect and assess safety signals
- Inform on product safety & performance
- Ensure safety requirements
Payers
- Determine value and coverage
- Monitor usage within the criteria
Physicians
- Support decision-making
- Improve care
- Inform real-world treatment alternatives
Patients & Advocacy Groups
- Understand treatments
- Perceive risks and benefits
- Outline the patient experience
Industry
- Meet safety, performance & effectiveness commitments and requirements
- Validate value
- Inform reimbursement
- Develop publications
Takeaways from the Panel Discussion
RWE is:
Clinical evidence
The panelists unanimously agreed with the initial quote that Real World Evidence (RWE) is clinical evidence. This statement is further strengthened by the surge of guidance on RWE across the globe, particularly in the last 3-7 years. Additionally, the panelists also shared the view that the concept of RWE is not new. However, what’s new are the methods adopted for gathering this information and the increased accessibility facilitated by digitization.
Part of the entire product lifecycle
Today, RWE plays a crucial role in everything from research and development to identifying unmet medical needs and seeing how products are used in clinical practice. It is vital to capture the total patient experience, understand the risks and potential benefits of products, and provide information where clinical trials are insufficient. Clinical trials are, by definition, limited in scope and populations and controlled for things common in real-world settings, such as complex patients with many comorbidities. RWE will help capture and integrate additional information to benefit patients.
In need of collaboration
Collaboration is crucial to avoid siloing and enhance transparency and trust. One opportunity to collaborate is when draft guidance is published for public comment. A successful example is the paper referenced by Estelle Frappé, which was published through a public-private partnership involving academia, regulators, and industry stakeholders. This collaborative community is vital to advancing regulatory science and harmonizing guidance and regulations across the globe.
Heather Colvin cited a successful example of a Johnson & Johnson study for label expansion in cardiac ablation that relied on collaboration: Although the company’s product had been on the market for paroxysmal atrial fibrillation (Afib), anecdotal evidence showed it was being broadly used in persistent Afib. Working collaboratively with the National Evaluation System for Health Technology (NEST), various data network partners, and the FDA, the company developed a robust clinical evidence study using only RWE from three different sites to win a label expansion. However, this took five years of intensive collaborative work, indicating that collaborative processes must be improved and accelerated.
Ideally, the goal would be to gather data and seek approval across multiple geographies to help patients throughout the globe. There is a long history of multi-stakeholder public-private partnerships, some of which are funded by regulators and health authorities in the US, Japan, China, and the EU.
Unevenly adopted across therapeutic areas
Cardiovascular disease (CVD) is one of the leading therapeutic areas for RWE, in part because this is an acute area with high risk – and potentially high benefit – for patients. Providing better information in a high-risk area like CVD is critical. In addition, there are many expansive registries, partnerships between clinical societies and regulators, etc., in the CVD space. RWD is also widely used in orthopedics, although not in the pre-market space. Expanding the use of RWD will require identifying underlying challenges.
In part, this uneven adoption is driven by different evidence needs. Thus, not all data is appropriate for different studies. Multiple data sources require review for suitability for a research question in terms of quality, completeness, privacy, the study population, etc.; Estelle Frappé illustrates the issue using a hypothetical example: If one were to choose a data set from Romania to inform a regulator in the US, there could be discrepancies because variables such as patients, the standard of care, products on the market, and how patients are treated are different. In addition, multiple data sets may need to be used to answer one question. This indicates that many questions need to be answered before conducting an RWE study with RWD is possible.
Asking the right questions and the issue of sample size
When using and developing data sets, asking the right question or questions at the outset is essential. It is also helpful to consider that the data might be used for other purposes. Therefore, it is better to add questions during data collection than attempt to gather additional data several months or years later. This makes good gap analysis and assessment from the outset indispensable.
The sample size is another challenge: attaining an appropriate sample size often requires vast, shared datasets. To use them, we need standard “data dictionaries,” i.e., terms that can be searched consistently to identify patients and products of interest. Dan Stephens shared the example of the REAL.PE study on therapies used to treat pulmonary embolism, which started with a large data network of 340,000 patients to find 1,200 patients that met the criteria for the study. Heather Colvin cites a planned pediatric study that could not achieve the needed sample size despite the participation of a series of pediatric hospitals.
In addition, there are challenges relating to capturing populations that are notoriously difficult to study, e.g., in pediatrics and rare diseases. Given the challenges associated with clinical trials in these populations, RWE could be a key tool to support better evidence generation.
Achieving diverse and equitable datasets
Karen Tkaczyk noted that one aspect of representative data sets can be appropriate ethnic or regional population diversity, and she asked the panelists how regulators and companies are working to advance diversity through language and culture. Dan Stephens cites Boston Scientific’s “Close the Gap” initiative, launched 20 years ago, to address health inequities across race, ethnicity, gender, and SDOH while increasing enrolment of diverse and underrepresented populations in clinical trials. This initiative has been going on for 20 years. From a societal and public health perspective, there is also the issue of the appropriate treatment of patient populations that may respond differently to different interventions.
Heather Colvin asserts that J&J is waiting for Congress’s new draft of diversity plan guidance, which requires companies to develop diversity plans as part of their clinical research. It prescribes that they not only determine the burden of disease in diverse populations but also translate this into recruitment and retention mechanisms. In addition, the company’s “My health can’t wait” initiative addresses community engagement to gather input on potential barriers based on location (urban vs rural). It strives to increase diversity and address language and cultural competency in recruitment and retention mechanisms.
Estelle Frappé would like to see these initiatives implemented in Europe, adding that this can be a challenge in settings where collecting data on race and ethnicity is often prohibited by privacy laws. Initiatives such as MedTech Color that provide tools and best practice documents are helping pave the way toward more diversity in the medical device industry.
AI-based Data Analysis
AI is already used to collect and analyze data from social media, publications, and studies. Regulations are also being implemented. However, due to AI’s explosive growth and development, regulators find it challenging to keep up. AI will undoubtedly be used even more in the future; however, closed-loop AI is more typical in this setting than the generative AI (e.g., ChatGPT), which we hear so much about currently in the media.
Building Trust – Together
The clinical trial space has long-standing mechanisms of trust built into the system, such as the early and public posting of protocols. In RWE, a corollary needs to be developed. Current guidance can serve as the foundation, and transparency and trust mechanisms need to be proactively and collaboratively developed, e.g., to offset concerns about data mining and industry players potentially analyzing and selecting data in a way that favorably portrays their product.
Key Takeaways
Panelists agreed that RWE is, in fact, clinical evidence. There are still many challenges surrounding its use and regulation that we should address collaboratively. Best practices must be established, and transparency is crucial to building trust in RWE. As Dan Stephen emphasized, “RWE starts out pretty messy.” Despite the immense challenge this constitutes, it is well worth the investment because of the tremendous opportunity RWE provides to improve healthcare – and potentially address equity issues – on a global scale. After all, as Heather Colvin concluded, the focus needs to be on patients and on viewing them not as mere data sources but as partners in research and people to be served.
During this second edition of our Life Sciences – In Focus series, we have seen a regulatory landscape rapidly shifting as it adapts to a flood of RWD and RWE in the age of AI. We will be closely monitoring future developments.
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