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The Cancer Letter – Real-world data, evidence should be leveraged in clinical research to better include and ultimately treat larger patient populations

The Cancer Letter – Real-world data, evidence should be leveraged in clinical research to better include and ultimately treat larger patient populations

The growth of personalized medicine in oncology continues to fuel a shift from traditional chemotherapies to immunotherapy. Currently, there are more than 30 immunotherapies approved for use in the United States, with more than 2,300 immunotherapy clinical trials listed on


However, significant disparities exist in randomized clinical trial participation with clinical trial populations skewing younger and healthier than real-world patient populations, with limited ethnic and gender diversity. In an analysis of more than 300 randomized clinical trials, the median age of trial participants was 6.49 years younger than the disease population.1


This disparity is particularly prevalent in oncology trials. For example, the median age of patients with advanced non–small-cell lung cancer is 70 years; however, the elderly are significantly underrepresented in clinical trials where the median age of participants is 60.9 years.­2 The use of younger patients in trials means that the safety and efficacy data are often not generalizable to the real-world population.


As a result of differences in comorbidity, functional status, toxicity, and pharmacokinetics associated with this age gap, the survival and disease progression benefits demonstrated in the younger clinical trial population may not be seen in an increasingly elderly population of patients with advanced NSCLS. Given this disparity, what are the best outcomes we can actually expect in clinical practice?

Expanding the use of pragmatic trials in oncology

While RCTs remain the gold standard for the assessment of safety and efficacy, the industry must identify alternate methods of gaining insight into treatment patterns and performance.


The complexity of the oncology treatment landscape offers an opportunity for the industry to expand the use of real-world evidence to provide critical information about how new treatments perform in real clinical settings. RWE has been used successfully to support new and supplemental indications. It may also have the ability to inform post-market use and safety monitoring as adverse events for new drugs may not emerge until the treatments are available in clinical practice, when a larger number of patients receive them and for a longer duration than in clinical trials.


As the FDA finalizes guidelines for the use of RWE to support regulatory decisions, we must also work look at expanding RCT eligibility to include real-world patient populations to capture data on the day-to-day usefulness of drugs.


Pragmatic trials offer the ability to test treatment options on more representative patient populations with fewer exclusions for common conditions that could affect outcomes in practice. The use of pragmatic clinical trials, focusing on the correlation between treatments and outcomes in real-world clinical settings, has increased significantly over the past several years, particularly in chronic diseases.


For example, AIRWISE and REDEEM are large pragmatic clinical trials designed to provide real-world data on broad populations of COPD and diabetes patients, respectively, not captured from traditional RCTs.


As pragmatic studies continue to demonstrate that real-world data can be analyzed and compared to outcomes expected under a RCT in other areas of medicine, it is reasonable to expect that they can be effectively used in oncology as well.


The FDA has signaled openness to this approach in its recent RWE Program Framework and the expansion of the RCT DUPLICATE demonstration project, designed to use RWE to attempt to replicate the results of 30 RCTs. The goal of this project is to help inform the FDA’s standards for using RWE in regulatory decision-making and identify when and where RWE can provide estimates of treatment safety and efficacy.

Assess data integrity and verification

To understand the treatment of oncology patients, it is critical to evaluate clinical data points as well as elements of the patient-centered outcomes, such as adherence rates, persistence and time on treatment, toxicities and functional status among others.


This information, along with key demographic and patient characteristics, is widely available through real-time tracking of clinical and claims data elements across unified electronic health record and reimbursement systems. Payers and other healthcare professionals have long used real-world data for health economics and outcomes research (HEOR) to make decisions about access, coverage, reimbursement and formulary placement for treatments.


In a recent blog article, the editor in chief of the ASCO Daily News suggested the industry needs a reality check about the use of RWD: “to be useful, the data need to be accurate, consistently collected, and verifiable to a level comparable with what we expect from a prospective clinical trial…Without doubt, there are highly reliable big data sets, derived from multiple centers, abstracted according to consistent validated protocols with robust quality assurance and verification strategies. These sets are a valuable resource with great potential for research and care delivery…Otherwise, we run the risk that incomplete or inaccurate data derived from inherently biased, or poorly characterized, patient populations gain a new respectability as real-world data.”


The ability to transition the use of RWD from patient management to observational studies to support regulatory filings will be based on validating data quality. McKesson is working with the Friends of Cancer Research, biopharma companies and other industry stakeholders to create rigorous standards for defining regulatory-grade data and quality assessment.


This starts with setting standards for data provenance, documenting the origin and tracing the lineage of the data. This includes validating structured data with chart notes to ensure that the data is consistent, complete and representative of the target patient populations.


The initial Friends’ RWE pilot project demonstrated that different datasets could be used to extract real-world endpoints in a consistent manner. In order to further characterize the role real-world endpoints may play in measuring treatment effect size, pilot project 2.0 will examine the ability of different real-world endpoints to detect treatment effectiveness in real-world patient populations.


In addition to rigorously maintaining quality, RWD must also be evaluated for the appropriateness of EHR data sets. While there are hundreds of EHRs in clinical use today, not all will be suitable for use as a source of RWD.


McKesson’s iKnowMed oncology EHR, which captures outpatient medical histories from community oncology practices treating approximately one million patients per year, has successfully been used to help understand the real-world utilization and outcomes associated with a number of oncology agents. However, that same data set may not have clinically relevant information regarding a cardiovascular therapy.

Emerging opportunities for RWE in oncology

RCTs remain the gold standard for gathering safety and efficacy data to support regulatory decisions; however, timely, cost-effective recruitment of representative patient populations is increasingly challenging in oncology.


As pragmatic studies continue to demonstrate the ability to analyze treatment patterns and outcomes in other areas of medicine, oncology should welcome the ability to use regulatory-grade data to gather evidence generalizable to oncology patients in real-world clinical settings.