FOCUS on methodology.

Given that more than 95% of cancer patients are treated in the real world , versus in clinical trial settings, it makes sense that the 21st Century Cures Act encourages the FDA to incorporate real-world evidence (RWE) into the drug development process. However, drug developers and other industry stakeholders are still in the formative stages when it comes to developing reliable models for generating RWE that is accurate, consistent and timely enough to be useful to the FDA’s review process.

Andrew Klink, PhD, MPH , who serves as Director of Real-World Evidence and Insights for Cardinal Health Specialty Solutions, recently identified nuances in physician-reported data collected for an RWE study that his team was conducting on a cancer therapy. This discovery, the results of which were published in the November 2018 issue of Future Oncology , led his team to develop a new, more reliable method for generating RWE, one that more closely aligns with the gold standard of patient response measurement typically used in clinical trials. To help us better understand the implications of Klink’s research and new data collection methodology, we connected with him to learn more. The following are excerpts from our conversation.

Andrew Klink: In 2016, after recently completing an RWE study to determine patient response to cancer therapy, our team was surprised by the findings. When we analyzed the physician inputs in aggregate, across a cohort of patients, we found that the proportion of patients with complete or partial response to therapy was much higher than the responses reported in the drugs’ randomized clinical trials.

We wanted to understand the reasons behind this variance, so we sought out qualitative feedback from the participating physicians, to learn what measures or tools they were using to evaluate patient response. We found that most participating physicians weren’t using standardized criteria at all; they were primarily using their own clinical judgment to evaluate patient response. For example, perceived clinical benefit demonstrated by improved lab values or less symptom burden might have contributed to a more subjective interpretation of response.

While the treatment of most forms of cancer can definitely be both an art and a science, it was inarguable that these methods for measuring patient response were far more subjective, and less consistent, than the gold standard, RECIST. The RECIST classification compares changes in a patient’s lesions and tumor sizes over time, and is the methodology typically used to measure solid tumor response to therapy in clinical trials. We hypothesized that the more objective RECIST approach would be likely to deliver more accurate patient response results than those formulated based on the individual clinical judgment of each participating physician.

AK: This approach is a little different than the way RECIST is facilitated in a clinical trial setting. First, in a clinical trial setting, the imaging results are typically analyzed by central review, where in a real-world setting, the imaging resides with and is analyzed by each patient’s treating physician and/or radiologist. Second, in a clinical trial, patient response data is usually analyzed at very specific, prespecified, consistent timeframes, for all enrolled patients. In real-world settings, the scans tend to be conducted largely in conjunction with each patient’s cycle of treatment, which may differ from patient to patient. So, it’s fair to say that using this methodology in a clinical trial setting likely further enhances the accuracy of the findings. However, given that real-world data (RWD) by their very nature must be collected and analyzed by a broader array of researchers, we feel that this approach substantially improves the overall accuracy and reliability of the data collected. 

AK: Our goal is consistent with the FDA’s; we always aim to obtain the most accurate and reliable data possible. So, this research served as the impetus for us to now use the RECIST classification for evaluating RWE that measures patient response to therapies that treat solid tumors.

We’ve also tested a similar approach to generating RWE in the hematologic malignancy setting, using the Lugano classification (Cheson) for patient response to therapy. We found a similar trend, regarding the overestimation of positive patient response by physicians, and we’re currently working on publishing the results of that research, too.

AK: The ultimate end goal of generating RWE is to ensure it’s actually useful to the FDA and other healthcare decision makers for labeling and other decisions. That means accuracy is key. Manufacturers are well served to report RWE in a way that’s as close as possible to the gold standard FDA reviewers are accustomed to dealing with in clinical trial settings.

In December 2018, the FDA released a framework for using RWD for regulatory decisions. In general, the framework shares criteria for improving the accuracy, reliability, recency and speed of data collected in real-world settings. The approach we’ve developed models the RECIST approach, but within real-world parameters, and allows for all the FDA’s recently released criteria to be met.

AK: Sure. Let’s start with its usefulness in premarket studies, when manufacturers are seeking to understand the current competitive landscape. This research indicates that more subjective physician-reported measures tend to overestimate patients’ positive responses to therapies currently on the market. Relying on those subjective measures alone can potentially make it more difficult to prove that new therapies can address unmet needs. Using this novel, more rigorous means of patient response evaluation not only delivers more accurate results, but also is likely to prove far more useful to manufacturers in search of data that can help them demonstrate that their therapies are addressing unmet needs.

Post-approval, we believe that manufacturers will be able to use this new method to demonstrate how their therapies are being used in the real world, in a manner that’s consistent with new FDA guidance, to ensure accurate and reliable data but at a fraction of the cost to conduct similar research in a clinical trial setting.