SummaryIn this third episode, Sam Ferguson, a consultant in Avalere’s Market Access practice, along with Allison Petrilla, a managing director in Avalere’s Health Economics and Advanced Analytics practice, and Amy Schroeder, a senior consultant in Avalere’s Market Access practice, discuss how stakeholders are utilizing and translating real-world evidence (RWE) into value for oncology.
Sam: Hello, and welcome to Avalere’s third episode in our Start Your Day with Avalere podcast series, focused on value in oncology. My name is Sam Ferguson, and I am a consultant in the Market Access practice here at Avalere. I am joined today by Allison Petrilla, a managing director of Avalere’s Health Economics and Advanced Analytics, and Amy Schroeder, who is an oncology pharmacist by training and a senior consultant in our Market Access practice at Avalere. In today’s episode, we will dive into how stakeholders are translating clinical evidence into determinations on value. Allison and Amy are both experts in this space and have led numerous analyses that have supported clients with real-world value in oncology. In the first episode of this series, we discussed different tools that are being used to identify high value therapies, including clinical pathways, compendia, and value frameworks. Allison, what are the types of evidence that the creators of these tools are leveraging when assessing the value of a therapy?
Allison: Hi, Sam! Great question. We all know that clinical trials are the “gold standard” for evaluating safety and efficacy of a product. But we are seeing a continued and increased investment in the generation of RWE to assess the value of these new and emerging therapies. This can include a lot of different things—like clinical and economic humanistic value drivers—that can be from a number of different methods like a prospective study, a retrospective observational study, looking at treatment patterns, survival analyses, or comparison of drug classes and drug groups, while also using things like retrospective claims data or electronic health record (EHR) data and economic modeling. There are several techniques we can use in the real-world setting to help us identify the potential value around the therapy. We have a webinar on Thursday, June 4, where some of our colleagues will have a deeper discussion on the analytic component of this topic.
On a high-level summary, RWE provides a unique opportunity to evaluate populations who might not otherwise be candidates for clinical trials. When thinking about cancer, this could be a cancer patient who is elderly and has other complex comorbidities, such as renal impairment or cardiovascular diseases, and allows us to identify the characteristics of these patients and treatment patterns to gain a greater understanding of how a product may perform, or how a tumor may respond in a real-world setting. It is a timely topic, especially as we are seeing how the current COVID-19 pandemic is impacting care delivery in vulnerable populations, in particular the oncology population, and emphasizes the need for RWE at the front and center.
An area where Amy and I spend a lot our time is working with linked data and piecing together information from medical claims, pharmacy claims, lab data, and EHR data, so that we can see a complete and timely picture around how oncology care is delivered and those outcomes might look like.
Sam: Thank you, Allison. I appreciate you setting the stage for us. Amy, what are your thoughts on some of these types of evidence and integration of RWE in these studies?
Amy: I have to agree that clinical trials still remain the primary source of data for clinical guidelines and compendia, which are two great tools used that start on the clinical side of things. We know payers are also using these tools, and the content of these tools is trickling down and affecting the content of clinical pathways, other clinical support tools, and value-based frameworks. To add on to what Allison has said, these tools are also integrating the actual RWE they are seeing in patients. For example, for clinicians who are using clinical pathway tools, they may also be integrating their own patient experiences—their own RWE. This is already trickling in and affecting these types of decisions, and as we look to what clinical pathways and other clinical decision support tools provide, they are looking at the care already mapped out in clinical guidelines and compendia, and then adding external expertise, and supplementing with real-world patient experience. This makes sense especially now that we are now looking at outcomes and value, to add the RWE element. Although we often set to follow clinical guidelines, real life as Allison mentioned does not always fit, and so it is nice to see RWE entering this space because we are noticing that in some of these tools, clinicians and others who build these tools are going back and seeing if real-world experiences should tweak some of the content they have in clinical pathways. It is interesting seeing what is coming about.
Sam: Thank you, Amy. So, understanding the myriad of information that is going into these tools and as we are talking about RWE, it is important to mention that stakeholders are not uniform in how they are using these data. Amy, could you speak to the varying ways in which RWE is being leveraged in the oncology value conversation?
Amy: Sure, Sam. For the clinical decision support tools that we mentioned, like clinical guidelines, pathways, compendia, and some other tools, we all know that they are built for a certain customer base and that can be a different customer base for each one. Some of those tools are built by payers for payer use. Some tools are built by a vendor who is selling to a payer. Additionally, we have some tools built internally by clinicians, and some that are built by a vendor and sold to clinicians.
With different audiences and different objectives, we know that the data can be used differently in those tools. We expect that we may see the RWE as it enters these spaces also being used different by these stakeholders. The big point from the research we have seen is that for RWE to be used more consistently and broadly, publication is key. As we see RWE trickling into tools right now, it is still on a small, personal basis. And if we want it to be used more broadly and consistently, so that we say cancer care is delivered consistently across the US, it important to get this information out there.
Sam: Thank you, Amy. I appreciate the forward-looking charge. Allison, what are some of your thoughts on this topic?
Allison: Thank you, Sam. Building on Amy’s comments around ensuring patients are receiving the best care delivery, although stakeholders are utilizing this information in various ways, they are all working towards a common goal: improving patient care and outcomes. For providers, their goal is to identify the best treatment options for their patients and leveraging information from these tools, published literature, clinical trials, and their own real-world experiences. For health plans, they want to ensure their beneficiaries have coverage for these treatment options and innovative therapies to help improve patient outcomes. These stakeholders are also looking at this evidence to determine how a product might fit into their formulary or treatment pathway to be utilized by providers in their network. Everyone is working toward this common goal, and that is nice in this RWE space. The evidence that is being generated and published is contributing to this knowledge base, which is really exciting.
From a life sciences perspective, if we think about manufacturer stakeholders, RWE is an opportunity to differentiate a product or class and show how a treatment may outperform the current standard of care. In some cases, there may not be a standard of care for a subpopulation. So, it is exciting to see the evidence emerge when we are looking at vulnerable populations, rare tumor types, and rare cancer types, and then use real world evidence to determine answers to questions like:
- Does drug X outperform drug Y?
- Does class X outperform class Y?
- How long are patients on treatment (i.e., days vs. months vs. years)?
- Are patients switching therapies?
- Are patients able to tolerate and continue a therapy to see a meaningful improvement in outcomes?
- Are patients able to have better outcomes from one therapy to another?
- What does treatment discontinuation look like?
- How does the performance change when we look at subgroups?
I mentioned vulnerable populations earlier. When you think of elderly populations, who in some cases may be too frail to tolerate certain treatments, how would a new treatment potentially change that paradigm? What would be the economic story? Is the economic story as compelling as the clinical story? This allows us to test these hypotheses of how a product, treatment, or intervention may perform in a real-world setting once we remove some of the guardrails that are typically associated with a very controlled clinical trial setting.
All of these learnings can be generated, published, and shared with stakeholders, working toward the common goal of improving patient care in oncology.
Sam: Thank you, Allison. It is very clear that you are both incredibly knowledgeable in this space. In addition, both of you lead much of our work in analyzing RWE at Avalere. Building on some of the questions that you teed up, Allison, can you speak to the questions you are looking to answer for clients and what approach you take in finding those answers?
Allison: Yes, happy to! Amy and I spend time looking in linked claims, EHR, and laboratory data to see if we are seeing what we expect to see. As we move out of that clinical trial setting, how are patients being managed? How does the real-world setting compare to the controlled clinical setting? What are the types of patients who are receiving treatment? Can we identify our population of interest? We know there are challenges in using administrative claims data in identifying certain disease states, but we have successfully, across several different tumor types and blood cancers, been able to identify population of interest, and tease out which treatments they are receiving; what first-line therapy and second-line therapy look like for a population; and what overall survival look like, using our Medicare data.
There are a lot of cool things we can do to expand the knowledge base using RWE. These are complex questions and require a sophisticated team of clinicians, health services researchers, and data scientists to dig into the information we have—particular when it is retrospective information—to build out the story for how cancer patients are being managed and treated. One thing we often see in our Medicare population is that many of the elderly patients with different tumor types and blood cancers are not receiving any aggressive therapy—so no chemotherapy or targeted therapy or immune-oncology therapies—being utilized, and perhaps it is due to the frailty of these patients, complex comorbidities of these patients, and tolerability issues. There are several factors that can contribute to these decisions, but it brings up the question: Is there more we can be doing in the oncology community to better manage these vulnerable populations? So, that is where we spend a lot of our time.
We also look at the characteristics of the prescribers and oncologists managing these patient populations. For example, we released a poster at the virtual International Society for Pharmacoeconomics and Outcomes Research conference that describes the characteristics of both patients and providers in a breast cancer population who are receiving innovative therapies. We did this across a number of payer types—like the Medicare fee-for-service population and managed care—and saw interesting differences in terms of who is being managed by academic medical centers and hospitals settings versus those being managed in the community setting. There is interesting information we can gleam through using administrative claims data and other linked information.
Another area Amy and I have spent several years looking into is how providers are managing patients in relation to published guidelines and pathways. We have an abstract for the American Society of Clinical Oncology (ASCO) conference that describes provider concordance with first-, second-, and third-line treatments in patients with metastatic non-small-cell lung cancer. The findings might surprise you. It is interesting when you look under the hood and really look at the claims to see how innovative and creative providers are in finding a meaningful solution to improve patient outcomes. I could go on and on, but I will give Amy to share her perspective on this.
Sam: Thank you, Allison. I also appreciate the teaser of what is to come at ASCO. Amy, what are some of your thoughts on this?
Amy: I will also add for the ASCO abstract, whenever I am on those types of projects, and given the disease, I am often asked what first-line regiments we should look for. I think what we are seeing after doing this concordance study is that we should take a step back and be open to what we see and what else we can identify. With all the expertise that we bring into these analyses, we can bring different perspectives of stakeholders into the conversation as well. We have oncologists, pharmacists, data scientists, and individuals with payer experience, all of whom can chime in and say what is affecting a certain audience. This broadens what we can do.
Another way on how we are using real-world data is sometimes we have life sciences clients that may say, “Why is my drug not matching the forecasted performance we expected?” And from there we can pull in all the data about that drug from diagnosis through the patient journey and determine the outcomes and cost. If we are looking at the population that qualifies for this drug, are all of the patients that we are seeing getting access to it? This is where it is interesting in the sense of mapping back to the treatment patterns and adherence to see possible reasons as to why the drug may not be performing in sales as originally expected.
A second project type that we work on is for a pipeline product as it is entering a particular space. We know what the space looks like in claims, but what would it look like if the client’s drug was available in the space today. We can pull in all the information and, based on clinical trial exclusion and inclusion criteria, identify populations that would qualify, compare against other drugs, and determine the population considerations and potential impacts. For clients whose drugs are at launch or close to launch and we know of the price of that drug, we can include cost information to understand downstream cost. It is a great way to calculate and convey information to help explain and shape assumptions to plan for the future.
And a third project example would be support for targeted therapies with clients who sell molecular and genetic testing. Not only are looking at the importance of testing but also the research with clinical decision support tools validating the need to do testing that informs a decision. We can look at claims and see when testing is done in a cancer, and then identify downstream effects of that test result. It is nice to have the other data sources like the EHR and lab data to determine the impact. Also, looking at the value of testing earlier, or if the type of the test is necessary. It is interesting to see the output of the work we do, and each project is slightly different and makes us better in the end because we are learning more and more as we go.
Sam: Amy, Allison, thank you both so much for your insights today. They are extremely valuable to our listeners. I appreciate you joining us today. Thank you all for tuning into Avalere Health Essential Voice. Please stay tuned for more episodes in our Start Your Day with Avalere series. If you would like to learn more, please visit us at www.avalere.com.
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