Randomized clinical trials (RCTs) use a well-established methodology for gathering robust evidence of the safety and efficacy of medical interventions. They remain the “gold standard” of data for evaluating new drugs. But clinical trial protocols, by design, are built with some inherent biases. For example, the patients selected to be enrolled in clinical trials are often not representative of the general patient population oncologists treat in clinical practice.1 By comparison, real-world studies aim to produce evidence of therapeutic effectiveness for patients in real-world practice settings. Real-world data (RWD) can provide information on the long-term safety and effectiveness of drugs in large heterogeneous populations, in addition to information on utilization patterns and health and economic outcomes. RWD is becoming more common and more widely accepted, with many stakeholders beginning to use real-world evidence as a complement to RCT evidence.1,2
Journal of Clinical Pathways (JCP) recently spoke with Edward Stepanski, PhD, chief operating officer, Outcomes Science and Services, Concerto HealthAI, and professor of internal medicine at the University of Tennessee Health Sciences Center (Memphis, TN), regarding the increasingly large role RWD is playing in oncology care. In April 2019, Concerto HealthAI announced a new partnership initiative with Astellas Pharma aiming to utilize RWD to help patients with FLT3 mutation-positive relapsed or refractory acute myeloid leukemia (AML). The initiative in AML will leverage American Society of Clinical Oncology (ASCO)’s CancerLinQ Discovery database, which contains de-identified cancer patient records and is used by leading academic researchers, non-profit organizations, government agencies, and industry.
For readers who may not be familiar, please tell us about Concerto HealthAI and what your specific role is there.
Dr Stepanski: Concerto HealthAI is a company dedicated to improving outcomes for cancer patients through the use of different sources of data to understand best practices and how to drive better outcomes. In particular, we are expert in the use of RWD. We have been working with RWD for quite a long time, particularly with clinical electronic medical record data to help us understand how patients not on clinical trials are being treated. This type of data is becoming extremely important for informing care.
In addition to aggregating those data and structuring those data in ways that help us understand patient care and how care is delivered—and where there are good and bad outcomes in terms of effectiveness and toxicity—we also have a team that does natural language processing and creates artificial intelligence (AI) algorithms to understand and create new data structures that we expect will give us insights into potential predictors of outcomes that may not be otherwise well understood.
Historically, we have been fighting upstream in using RWD, because there is an important tradition that all knowledge comes from RCTs. This is the idea that it is only through RCTs that we can learn anything to allow us to create new guidelines, approve new drugs and new treatments, understand what treatments are best, and so on.
But there has been an appreciation, recently, that RWD has a unique role to play and a unique story to tell that is extremely important in understanding how care can be delivered in the best way. In particular, there are two areas where RWD are different, which has exposed some of the shortcomings of the RCT.
One of those important areas is that the care delivered to a patient when they are enrolled on an RCT is very different from the care that is delivered outside of trials, ie, just standard of care for busy clinicians. On a clinical trial, you have a research team that is delivering a protocol-driven treatment. They are evaluating toxicity and safety signals at each and every visit according to strict protocols, grading these toxicities and intervening when needed. They are also making dose changes in response to toxicity signals that are prescribed according to protocol and then re-escalating dosages later if there is resolution of certain safety signals. In addition, they are performing other kinds of assessments on a routine basis that are very intense and not routine for patients not being treated on a trial. We see patients getting through RCT therapy, potentially, because of all this additional evaluation and support in ways that are not analogous to what would happen in clinical practice.
In standard-of-care clinical practice, if a patient has a grade 3 or grade 4 toxicity on a specific regimen, there is a reasonably good chance that that treatment will be discontinued, and they will be switched to a new treatment. There is not going to be the same effort paid to try and continue on that regimen with a number of different dose changes, institution of supportive care, and manipulation of contextual factors as there would be for patient on a clinical trial. The outcome might well be different; a different number of patients will actually complete therapy and get the full antineoplastic benefit in the RWD as compared to what happened on a clinical trial. You cannot necessarily extrapolate the results of the clinical trial to what is going to happen once the drug is approved and in the hands of busy clinicians.
The second important distinction between the data types is that the patients who make it on clinical trials tend to be the healthiest patients with that specific disease, which, in the case of cancer care, can be extremely meaningful—ie, that patients with comorbidities or who have other kinds of abnormalities are not eligible for the clinical trial. Yet, once the drug is approved, it is given to all those patients that have comorbidities, have abnormal lab values, and have other kinds of vulnerabilities that may cause them to respond differently to that medication. The RWD gives us insight into what the true rate of effectiveness and toxicity might be in this therapeutic indication in a variety of patients who are not in the hands of the clinical research team. We are able to learn about the treatment in ways that you just cannot see in an RCT because those biases are built into them.
A good example of that would be some work that we did over the past year in working with a team that included investigators from ASCO and investigators from the Food and Drug Administration, where we looked at delivery of immune checkpoint inhibitors in patients who had a preexisting history of autoimmune disease. The patients were those who were excluded from clinical trials because of the concern that they might trigger an immune response that would be unfavorable to them—potentially catastrophically unfavorable to them—and trigger their underlying autoimmune disease. Yet, what we found when we looked at the RWD is that about a quarter of patients who got immune checkpoint inhibitors had such a history and so were being given these agents without really having been tested previously to understand if they were at increased risk or not. This is a perfect example of how RWD can be leveraged to understand and answer an important clinical question.
Certainly, these immune-oncology drugs have revolutionized and improved outcomes in a number of solid tumors in ways that are spectacular. We would not want to deny patients access to this treatment modality on the basis of a concern that may be unfounded. In our preliminary analysis, we actually found that they have the same outcomes as patients without autoimmune disease. But we need to investigate that further and look at specific disease processes and not just the global view to see if there are more nuanced insights that we can gain.
Let me give you another case example of the kind of question that probably will never be explored in a RCT. I would not enroll patients to a study and expose them to a drug that I think might do them harm, but to the extent that patients did get exposed because there was an assessment made by the health care team that the risk was small compared to the risk of their disease, I am able to then evaluate outcomes in that clinical setting. We can evaluate the outcomes of that exposure and evaluate an agent that might convey a great deal of good. We can accumulate that data, assess it, look at the clinical characteristics of those patients, and make some judgments about how that turned out for a clinical scenario that will never be studied in an RCT given the perceived risks.