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How Real-World Evidence Can Spur Collaboration and Changes in Oncology

Evidence-Based OncologyFebruary 2024
Volume 30
Issue 2
Pages: SP173-SP175

The fight against cancer is complex and multifaceted, requiring a collective effort to create transformative change. There is a vast opportunity for collaboration to spark discussions and advance research, which will not only positively impact cancer treatments but also the socioeconomic factors at play, including the costs and accessibility of treatments available. This begins with the way we generate and leverage real-world data (RWD).

Zhaohui Su, PhD | Image: Ontada

Zhaohui Su, PhD | Image: Ontada

According to the FDA, RWD provide invaluable insights into the effectiveness, safety, and value of medical treatments and interventions.1 The volume of RWD has increased rapidly in the past decade and is expected to continue to grow exponentially. The market for RWD is projected to reach $4.07 billion by 2030.2 Multiple framework and guidance documents have been published to recommend or require a standardized structure for RWD. These include the Health Level 7 International Fast Healthcare Interoperability Resources standard, the Observational Medical Outcomes Partnership Common Data Model, and the Clinical Data Interchange Standards Consortium.3-5 These structures were created to ensure proper data exchange, integration, communication, programming code sharing, and standardized quality checks of RWD regardless of the local electronic health record (EHR) system in place.

Building common data models following industry data standards for data quality and interoperability is imperative. This allows for effective collaboration across the field of oncology. As current standards continue to grow and evolve, there is still much to be done to ensure existing RWD and real-world evidence (RWE) are being leveraged to their full potential. Policy makers and partner organizations must endeavor to use these vast data sets to drive meaningful changes.

Why RWE Is Important for Changes in Oncology

Randomized controlled trials (RCTs) are considered the gold standard in evaluating drug efficacy and safety, but because RCTs are conducted in a controlled setting, they may not be generalizable to all patients, and in some cases would not be ethical or practical.6-8 In an area where RCTs continue to be used for quicker clinical results, those trials must be supported by reliable evidence. RWE provides helpful information that complements RCT findings and may help fill knowledge gaps related to how a medication is used in real-world medical settings.

For example, the clinical development of medicine needs reliable evidence to meet the requirements for regulatory assessment. Beyond clinical trial results for efficacy and safety of the investigational drug, key stakeholders must understand the safety of the treatment for diverse groups of people, the limitations of the potential treatment, and the realities of its accessibility to patients, as well as the final financial costs to the patient. This is where RWE can be used to fill gaps to inform decision-making and bring to light any challenges of certain treatments outside of a trial setting that may impose barriers to care in the real world.

The limitations of RCTs and the need for RWE have been well-recognized by the FDA as well as the European Commission. In 2018, the FDA Real-World Evidence Framework was created to evaluate the use of RWE throughout the drug development process.9 Specific to oncology, the FDA created an Oncology Center of Excellence (OCE) and launched the Oncology Real World Evidence Program to foster collaboration among regulatory and scientific groups and generate RWE for regulatory purposes.10 In addition to advancing regulatory policy, the strategic priorities of the OCE include collaboration through strategic partnerships across the FDA, federal agencies, and public-private partnerships to foster pragmatic use of RWD and accelerate the growth of RWE in oncology through leadership and training. This program is an incredible resource and base for building oncology collaboration in the US and is a prime example of how RWE could be leveraged by individual oncology and research organizations and/or policy makers in order to foster real change at the ground level for patients with cancer and their providers.

The Benefits of RWE

As mentioned, RWD and RWE have become a growing focal point for many stakeholders in health care. The following are 3 primary use cases for RWE in oncology that support industry collaboration and improvement:

RWE may help stakeholders make more-informed treatment decisions regarding approved medicines or medical devices.

The main reason RWD is used is that the data can fill evidence gaps from an RCT and provide insights into unanswered clinical questions for which the RCT may not have provided sufficient data. These areas include rare cancer types that aren’t well studied, certain subgroups that may not have been well-represented in trials, and long-term adverse effects that are revealed years after the RCT has ended. Filling these knowledge gaps helps patients, providers, caregivers, and regulatory agencies determine treatment effectiveness and safety in real-world settings.

RWD can help inform which medicine is more appropriate for certain patient subgroups that may not have been well-represented in RCTs. This allows oncologists to recommend more precise treatments for their patients based on how effective a treatment is across certain demographics and a variety of health factor combinations. This way, the best treatment can be delivered to the right patient at the right time. Real study examples of this use case as recently published in oncology journals have led to understanding treatment patterns and outcomes for patients with a rare and aggressive soft tissue sarcoma being treated in community settings or measuring turnaround times for biomarker testing ahead of first-line treatments.11,12 More recently, with the fast-growing genomics RWD, precision medicine is receiving unprecedented attention and is considered by many health care professionals as the future of medicine.

RWE can be used to accelerate development of new medicines and medical devices.

Gathering enough reliable data as required in a clinical trial can take several years; however, the oncology industry is beginning to accumulate myriad rich data sets that have the potential to expedite the drug development cycle, particularly through the use of RWD and RWE.

Between January 2019 and June 2021, 85% of FDA-approved applications for new drugs had incorporated RWE in some form, and more than half of those approvals noted that the RWE influenced the FDA’s final decision.13 An associate director for pharmacoepidemiology at the OCE recently attributed to RWE the successful approval of the first therapy for hormone receptor–positive/HER2-negative advanced or metastatic breast cancer with a PCK3CA mutation.14 Because this cancer affects just 14 of every 1 million individuals, a randomized trial would be impossible to conduct.

Further, results of a 2023 study indicated that RWD gathered from community-based oncology EHR platforms could feasibly emulate RCT eligibility criteria to create external control groups for clinical trials.15 Another study from 2023 concluded that RWE could reach similar conclusions to RCTs if emulated closely enough.16

Real-world studies are not meant to replace clinical trials. Instead, with the implementation of data standards, improved data completeness and quality, advancement of analytic methods and new techniques, a lot can be accomplished via real-world studies to supplement, echo, and reinforce the validity of trial results in the drug and device development cycle. This would help with regulatory decision-making and getting effective treatments to patients faster.

RWD and RWE provide insights that can help reduce health care costs.

Payers are turning to RWD and RWE to better understand which medical products may reduce health care costs. Clinical trials are primarily focused on efficacy and safety, and, because of their specific protocols or trial duration, they might not demonstrate or explore other benefits of a medicine, such as reducing the time a patient spends in the hospital or reducing the risk of ending up back in the hospital after treatment. The RWE generated following these trials can offer the insights that stakeholders want to see regarding overall quality of life and health care costs.

Furthermore, payers and policy makers can use RWE to evaluate what cancers or other diseases may benefit from cost reduction programs or better health care policies to prevent financial barriers to treatment. For example, a 2023 study of real-world outpatient cost of care for patients with breast cancer treated in community settings showed that there was a significant increase in outpatient costs over the previous 5 years, from $900 per patient per month (PPPM) in 2015 to $2800 PPPM in 2022.17 Documented results such as this show how costs changed over time and help empower stakeholders and policy makers with evidence when discussing positive changes.

Best Practices for RWD and RWE Solutions

Framework for assessing RWD and RWE quality

To ensure the reliability and validity of RWE, the data used to generate the evidence must be held to a high standard. For evidence-based oncology, several checklists exist for various study types to ascertain the quality of the data used and help to ensure consistency.18

A comparison of published frameworks for assessing the quality of oncology RWD and RWE, as presented by Ontada in 2023, highlights 9 quality domains for oncology studies with 6 domains for assessing RWD and 3 domains for assessing RWE quality.19 In addition, 2 recent frameworks for assessing fit for purpose and generating valid evidence for research and regulatory uses have been proposed by Desai et al and Gatto et al.20,21

FDA guidance for the industry

Regulatory agencies play a critical role in evaluating the safety and efficacy of cancer treatments; their standards for assessing RWD from electronic health records and medical claims should be considered when designing an RWE study. The FDA provides guidelines for those looking to submit RWE in support of a drug or biological product application, to ensure a high and consistent standard of RWD and methodologies among applications and encourage high-quality studies.1

Analytic methodology

Real-world studies are at risk of biases such as selection bias, information bias, and confounding. If not adjusted appropriately, these biases will threaten the validity of the study findings.Rigorous statistical analyses with appropriate methodologies will turn quality RWD into strong RWE. A recent framework from the National Institute for Health and Care Excellence offers guidelines for assessing data suitability, minimizing the risk of bias, evaluating the robustness of results, and ensuring transparent reporting.22

Novel and advanced analytic methods for handling biases and missing data are available.23 These include but are not limited to propensity scores, multiple imputation methods, time-dependent regression analysis, interrupted time series analyses, and generalized linear regression analyses. Important information about a patient’s treatment and outcomes in oncology is in unstructured data. Machine learning (ML) and natural language processing (NLP) are increasingly used to assist expert abstractors and scientists in generating RWD and RWE. Using NLP and ML to extract clinically meaningful information from unstructured EHR documents can produce high-performance output variables, as shown in results of a study recently published in Frontiers in Pharmacology.24 The hybrid approach that combines abstraction by clinical experts, ML, and NLP can generate a multitude of research-ready variables that can help answer important scientific and policy questions.

Importance of collaboration

Public and private entities have collaborated and shared their standardized definitions and methodological recommendations, such as The Friends of Cancer Research Real-World Data Collaboration Pilot 2.0: Methodological Recommendations from Oncology Case Studies.25 Another collaborative study evaluated the reproducibility and performance of real-world overall survival across 5 real-world data sets of patients with metastatic non–small cell lung cancer receiving chemotherapy or PD-1 combination therapy after applying selected clinical trial inclusion/exclusion criteria.26 These illustrated the power of multistakeholder collaboration to identify the challenge and importance of methodological rigor in the generation of high-quality RWE.


The generation of RWE holds immense and growing importance in oncology and is an important vehicle to drive collaboration and improve care delivery. Through RWE, stakeholders gain comprehensive, pragmatic insights into treatment outcomes parallel with or following RCTs. In addition, RWE can identify areas for improvement in health care, better inform regulatory decisions and policy changes, and enable better long-term patient-centered care. Oncology stakeholders can bring industry changes to fruition by leaning into RWD, creating a culture of deep collaboration, and leveraging new methodology and techniques to produce quality RWE. If the power of RWD and RWE in oncology is embraced, we can transform the way we understand, treat, and improve the lives of individuals affected by cancer. 

Author Information

Zhaohui Su, PhD, is vice president of biostatistics for Ontada. He is a scientific leader with 25 years of experience in applying statistical, epidemiological, and machine learning methods to real-world studies.


The views and opinions expressed in this article are solely those of the author, who is an employee of Ontada, and do not necessarily reflect the official policy or position of Ontada. The information provided in this article is for general informational purposes only and should not be construed as professional advice. The author and Ontada make no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, suitability, or availability of the information contained in this article. Any reliance you place on such information is therefore strictly at your own risk. In no event will the author or Ontada be liable for any loss or damage arising from the use of this article.


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