Health technology assessment (HTA) agencies are increasingly turning to real-world evidence (RWE) to broaden their evidence base for decision-making. As the science and landscape of RWE evolves, there are plenty of opportunities for biopharma and HTA agencies to work together to maximize the value of these data-driven insights.
In September 2019, we asked Jens Grueger, Ph.D., affiliate professor at the University of Washington, former head of global access at Roche, and president-elect of the Professional Society for Health Economics and Outcomes Research (ISPOR), about the history and current status of RWE in HTA decision-making, and how HTA use cases for RWE will evolve in the future.
This interview has been edited for clarity and length. Dr. Grueger’s views are his own and do not represent the views of ISPOR.
Q: As a leader in the health economics and outcomes research (HEOR) and market access fields for more than 20 years, how have you seen the role of RWE in decision-making evolve over time?
A: My first encounter with real-world data (RWD) was in 1985 when I was sitting in the basement of one of the large German insurers, looking at paper claims, and putting them in order so we could do statistics on direct utilization. It was the first time that these things had been done in Germany. It was a big project.
From the beginning, real-world evidence was something we used to better understand what was happening in real clinical practice: how are people doing, what are they doing, and what is the variation? That was the starting point. The problem was, of course, the analyses were exploratory, and although the Internet expanded data access around 2000, the analysis quality at the time was poor.
The first major success of real-world data was Vioxx, where in 2004 researchers in North America identified rare cardiovascular side effects that weren’t visible in clinical trial data. After that, things changed. Regulators began using real-world data more often. Today, things have changed as we’ve gained access to a greater breadth, depth, and quality of data. We can now generate regulatory-grade data that is fit for decision-making.
On the HTA side, payers are now speaking about moving from health technology assessment to health technology management. This means they are interested in getting more data about how drugs are being used, and which treatments should be prioritized for investment. We are seeing the first benefits in the HTA space, and we will see more RWD to help improve patient care in the future from wearables and sensors, for example.
Q: Can you provide a recent example of an HTA using RWE in decision-making?
A: Real-world data is not a replacement for randomized controlled trials, but there are many situations where you cannot do a well-controlled clinical trial. And when there is a high need and strong signal of evidence, RWE is fit for purpose.
For example, at Roche, we used real-world data to extrapolate evidence beyond the clinical trial. So, for our cancer immunotherapies, we evaluated durability of treatment effect. Payers believed, based on past cancer research, that you could always assume that survival moves towards zero. But with immunotherapies, there is evidence that patients actually survive longer and stay above the zero line, and some patients are cured. We used RWE from a cancer registry to convince the U.K.’s National Institute for Health and Care Excellence (NICE) that our more positive assumption was actually realistic.
Q: How biopharma and HTAs using RWE today?
A: Real-world evidence is a tool. You first have to be clear about what your research objective is, then identify the best evidence, whether from RCTs or RWE, to meet your objective. With this in mind, RWD currently plays three main roles for biopharma and HTAs:
- Informing clinical trial design and innovating the clinical development process. With RWE, we can better design clinical trials, and we gain more information about the distribution of patients and the history of the disease we’re studying.
- Accelerating and sustaining access to treatment for patients. Real-world data can answer HTAs’ and payers’ questions about how products perform in the real world, what the clinical benefit of a therapy is, and what the cost implications are.
- Improving patient care. We see drugs tested in one population in a clinical trial, but RWD can show how drugs perform when combined with other treatments or in different populations. This will help find the right dosage or treatment protocol for patients.
Q: What are likely barriers to maximizing the utility of RWE, and how can biopharma address them?
A: I think the underlying issue is credibility and trust. There is concern from regulators and HTAs that the industry is manipulating the data, so absolute transparency and practical approaches are important. Presenting protocols, methods, and data to decision-making bodies helps prove RWE’s credibility.
Another potential issue is risk aversion from biopharma when it comes to using RWD. Pharma takes significant risk when they invest in their clinical development programs, but that willingness to take risks often decreases throughout the product life cycle. I think over time we will see more use cases in which pharma is using RWE, identifying why an RCT wasn’t possible, and showing how they were able to get a product to regulatory approval or market access faster because they used RWE or a combination of RWE and RCT data. These use cases will demonstrate the utility of RWE and entice others to take risks.
Q: What is the future of RWE?
A: In pharma, we are all wondering what the year 2030 will look like in terms of accelerating development, availability of computing power, and data. From the real-world data perspective, I don’t think we can imagine what 2030 will look like.
For example, we can now generate data from our iPhones, or data we can combine with sensors to study how people are walking, where they have regular steps, how their blood pressure changes. We will have access to all of this in the future; it will be a brave new world. Not everybody is happy about that, and of course privacy will become an increasingly important concept. But real-world data and the large computing power we have will really change how we all think about drug development.