RWD Helps Researchers Include More Patients In Lymphoma Research
A conversation between Lymphoma Research Foundation Scientific Advisory Board Chair Andrew Evens, DO, MBA, and Clinical Leader Executive Editor Abby Proch
Randomized Phase 3 trials remain the gold standard in lymphoma research, but researchers are finding they often don’t reach the patients they intend to treat.
Now, the integration of real-world data (RWD) with traditional clinical trials is reshaping how new studies are designed to better study new therapies in patients that desperately need them. RWD helps fills the gap of a Phase 3 trial by capturing broader patient experiences, long-term outcomes, and quality-of-life insights that trial data alone can’t.
In this Q&A, Andrew Evens, DO, MBA, explores how outcomes research and collaboration across academic networks, registries, and industry partners are advancing more inclusive, data-informed approaches to lymphoma care and future trial design.
Clinical Leader: How is RWD used to identify gaps in current lymphoma care pathways, and how does that influence the design of interventional studies?
Evens: When it comes to treating lymphoma — and cancer more broadly — the gold standard is a prospective clinical trial, often a randomized Phase 3 trial. However, clinical trials don’t always represent the real world.
In part for safety reasons, trials have strict eligibility criteria. In many ways, you have to be a relatively healthy cancer patient to qualify — with good blood counts and near-normal liver, kidney, and heart function. As a result, many patients in the broader population don’t meet those criteria.
That raises an important question: If a therapy shows positive results in a trial, do those results apply to everyone, including patients who may be less physically robust or have other health issues?
Real-world data can help address that gap by showing how treatments perform across broader patient populations. In addition, RWD can also provide insights into rarer lymphoma subtypes and clinical scenarios. Clinical trials are typically designed around more common diseases, and for many rare conditions, interventional studies may never be feasible.
How can outcomes research help us understand the mismatch between regulatory endpoints and patient-centric outcomes in real-world settings, and how should trial design adapt accordingly?
There are clinical outcomes to consider, as well as economic and quality of life aspects. For many cancers, particularly indolent lymphomas that can occur for decades of a patient's life with periods of remission and relapse, quality of life is extremely important.
For a particular therapy of lymphoma, if treatment effectiveness is 1A, then quality of life is 1B. Our goal is to have our cake and eat it, too: We always desire a therapy that’s highly effective but also has a low side effect profile and preserves good quality of life.
We’re embedding quality of life measures more often into clinical trials. It’s harder to capture those measures in real-world or retrospective data because they rely on validated questionnaires and patient-reported outcomes, though some medical records now allow those to be included.
This is especially important in indolent lymphomas, such as follicular lymphoma, marginal zone lymphoma, and CLL. We want to understand not only how well a treatment works but also the severity, duration, and impact of side effects on patients’ lives.
What are the key methodological challenges when integrating RWD with prospective trial data (e.g., data harmonization, bias, confounding), and how can operations teams mitigate them?
The term “real-world data” has really emerged over the last decade, but there are different forms of it. One long-standing approach is retrospective data, where researchers go back through medical records — from one center or many sites— to study events or conditions that occurred in the past. That can be useful because it allows researchers to examine uncommon scenarios or include patients who may not qualify for clinical trials.
But retrospective data also have limitations. Because the information was collected in the past, it may not be standardized, and there can be missing data, bias, and no central review of imaging or pathology. The quality of data collection can also vary depending on who is collecting it.
Even with those limitations, retrospective data can be valuable when analyzed across large populations with strong statistical methods. However, it is often hypothesis-generating, meaning the findings should ultimately be tested in prospective studies or clinical trials.
There are also prospective forms of RWD. Some societies and foundations collect large data sets that include all patients rather than strict inclusion or exclusion criteria, and institutions often maintain survivorship or cancer cohorts that follow patients for long periods.
That long-term perspective component is important because many clinical trials evaluate an intervention for only three to five years. RWD can help answer what happens afterward — at 10, 15, or even 20 years.
How do you balance the need for rapid, adaptive RWE generation with the rigor and pre-specification required for regulatory acceptance in studies?
Some of the limitations I mentioned are why there is sometimes caution around regulatory acceptance of RWD.
However, if RWD are collected in a highly structured way — using tools such as a data dictionary and a common data model — they can be standardized across sites and analyzed more rigorously.
When that type of prospective RWD collection is well designed, it may meet the level of pre-specification or regulatory acceptance needed for conditions that are difficult to study in traditional interventional trials, whether because the disease is rare or there are safety concerns.
This research also doesn’t have to focus only on treatments or new medications. It can examine prediction models, prognostication, minimal residual disease, or specific imaging findings.
What ultimately makes rigorous RWD possible is collaboration — across sites, universities and foundations, and ideally across countries. Many of the scientific challenges we’re addressing are global, and working together allows researchers to generate stronger data at a larger scale and more quickly.
Finally, what role does collaboration among academic centers, site networks, and patient registries play in scaling RWE into actionable design changes for upcoming studies?
Collaboration is essential — not just across academic centers in the United States or North America, but globally.
One example in lymphoma research is the HOLISTIC Consortium for Hodgkin lymphoma, which stands for Hodgkin Lymphoma International Study for Individual Care (hodgkinconsortium.com). The consortium has collected data on more than 30,000 Hodgkin lymphoma patients worldwide. Using a structured approach with a data dictionary and a common data model, the effort combines data from clinical trials and prospective registries to better understand treatment patterns and support more informed personalized care.
The Lymphoma Research Foundation has been a strong partner in this work, which also includes gathering patient perspectives on these types of data collection efforts. Foundations and registries help scale these initiatives strategically, ensuring data are collected in ways that meaningfully advance research.
Greater collaboration could also include broader data sharing with pharmaceutical companies. After trials are completed, large amounts of data remain siloed, and expanding access could help researchers combine data sets and generate insights that improve patient care.
About The Expert:
Andrew M. Evens, DO, MBA, MSc, is deputy director for clinical services and chief physician officer at the Rutgers Cancer Institute and the Jack & Sheryl Morris Cancer Center, and associate vice chancellor for clinical innovation and data analytics at Rutgers Health. He also serves as medical director of the oncology service line and oncology lead for the combined medical group at RWJBarnabas Health, overseeing integrated cancer care across 16 acute care hospitals and more than 40 ambulatory oncology practices in New Jersey. An internationally recognized expert in lymphoid malignancies, Dr. Evens focuses on novel therapeutics, predictive modeling, and personalized medicine. He has maintained continuous NIH and NCI funding since 2005, served as principal investigator on more than 80 prospective clinical trials, and authored over 250 peer-reviewed publications. He is Editor-in-Chief of the British Journal of Haematology and holds multiple national leadership roles in lymphoma research