Real-World Evidence in Oncology: How Real-World Data Is Transforming Cancer Care

Real-World Evidence in Oncology: How Real-World Data Is Transforming Cancer Care

Introduction

Cancer care is becoming increasingly data-driven. Advances in molecular diagnostics, imaging, electronic health records, cancer registries, treatment monitoring, and digital health technologies are generating enormous amounts of information about patients and their experiences throughout the cancer care journey.

Clinical trials remain essential for determining whether a cancer treatment works under carefully controlled conditions. However, clinical trials do not always capture the full complexity of routine cancer care. Patients treated in everyday clinical practice may differ in age, comorbidities, previous treatments, socioeconomic circumstances, disease characteristics, and treatment preferences from the populations enrolled in clinical studies.

This is where real-world data (RWD) and real-world evidence (RWE) are becoming increasingly important in oncology.

The U.S. Food and Drug Administration (FDA) defines real-world data as information relating to patient health status or healthcare delivery that is routinely collected from sources such as electronic health records, medical claims, registries, and digital health technologies. Real-world evidence is the clinical evidence about the use, benefits, or risks of a medical product generated through analysis of such data.

In oncology, real-world evidence can provide insights into how cancer treatments are used, how patients respond in routine practice, which adverse events occur, how treatment patterns change, and how outcomes may differ across patient populations.

The growing integration of RWE into oncology does not mean replacing randomized clinical trials. Instead, it provides an opportunity to complement clinical trial evidence with information gathered from broader and more diverse patient populations.

As cancer care becomes more personalized and complex, real-world evidence may play an increasingly important role in understanding what happens to patients beyond the controlled environment of clinical research.

 

What Is Real-World Data in Oncology?

Real-world data refers to health-related information collected during routine healthcare or through other real-world settings rather than exclusively through traditional clinical trials.

In oncology, RWD can come from many sources, including:

• Electronic health records
• Cancer registries
• Medical claims databases
• Pharmacy records
• Laboratory systems
• Imaging systems
• Patient-reported outcomes
• Digital health technologies
• Wearable devices
• Molecular and genomic databases
• Hospital databases
• Disease-specific registries

The value of these datasets depends on their quality, completeness, relevance, consistency, and suitability for the research question.

The National Cancer Institute highlights electronic health records as one major source of oncology RWD and notes that information collected during routine care can help researchers understand patient experiences outside clinical trials.

However, raw data alone does not automatically become evidence.

Researchers must carefully define the research question, evaluate the quality and relevance of the data, select an appropriate study design, address missing information and potential bias, and apply appropriate statistical methods.

The resulting clinical insights can then contribute to real-world evidence.

 

Real-World Data vs Real-World Evidence

Although the terms are often used together, RWD and RWE are not interchangeable.

Real-world data is the information collected from real-world healthcare settings.

Real-world evidence is the clinical evidence generated by analyzing that data to answer a specific research question.

For example, an electronic health record may contain information about a patient's diagnosis, treatment, laboratory results, imaging, and outcomes. That information represents RWD.

If researchers analyze data from thousands of patients to evaluate treatment patterns, survival outcomes, or safety signals, the resulting findings may contribute to RWE.

This distinction is important because simply possessing a large database does not guarantee reliable evidence.

The analytical methodology and fitness of the data for the intended question are critical.

 

Why Is Real-World Evidence Important in Oncology?

Cancer is highly complex, and treatment decisions increasingly involve multiple therapies, biomarkers, diagnostic technologies, and patient-specific considerations.

Real-world evidence can help address questions that may not be fully answered by traditional clinical trials.

Understanding Routine Cancer Care

Clinical trials often follow strict eligibility criteria and treatment protocols. Routine oncology practice can be considerably more diverse.

Real-world evidence can provide insight into how treatments are actually used across different healthcare settings and patient populations.

Evaluating Treatment Patterns

RWE can help researchers understand:

• Which treatments are being used
• When treatments are initiated
• How treatment patterns vary
• How therapies are combined
• How frequently treatment changes occur
• Why treatments may be discontinued
• What happens after progression

These insights can help researchers understand the practical realities of cancer care.

Studying Patient Outcomes

Real-world datasets can be used to examine outcomes such as:

• Overall survival
• Disease progression
• Treatment duration
• Hospitalization
• Adverse events
• Healthcare utilization
• Treatment discontinuation
• Patient-reported outcomes

The ability to analyze these outcomes across broader populations can provide valuable complementary evidence.

 

Electronic Health Records and Oncology Research

Electronic health records are among the most important sources of real-world oncology data.

An EHR may contain information collected throughout a patient's clinical journey, including diagnosis, pathology, laboratory testing, medication history, treatment administration, imaging, clinical notes, and follow-up information.

This creates opportunities for researchers to examine cancer care over time.

However, EHR data also presents major challenges.

Healthcare professionals may record similar information in different ways. Some information may be structured while other information exists as free-text clinical notes. Important variables may be missing or inconsistently documented.

NCI's Real-World Data Program specifically focuses on improving the quality, completeness, collection, and exchange of patient data from routine clinical care.

For oncology research, improving interoperability and data standardization can make it easier to combine information from multiple institutions and healthcare systems.

 

Cancer Registries as a Source of Real-World Evidence

Cancer registries provide another important source of real-world oncology information.

Registries can collect structured information about cancer diagnoses, disease characteristics, treatments, and outcomes across populations.

They can support research into:

• Cancer incidence
• Disease patterns
• Treatment utilization
• Survival
• Population-level outcomes
• Differences between patient groups
• Rare cancer populations

The FDA has published guidance addressing the use of registries to support regulatory decision-making concerning the effectiveness or safety of medical products.

Registry data can therefore contribute to evidence generation when the data source and research design are appropriate for the intended purpose.

 

Patient-Reported Outcomes in Real-World Oncology

Cancer care involves more than tumor response and survival.

Patients may experience fatigue, pain, nausea, psychological stress, functional limitations, and other symptoms that may not be completely captured through conventional clinical measurements.

Patient-reported outcomes can provide direct information about how patients experience their disease and treatment.

Digital platforms increasingly make it possible to collect patient-reported information remotely and repeatedly.

Potential areas of measurement include:

• Symptoms
• Quality of life
• Treatment side effects
• Physical functioning
• Emotional well-being
• Treatment satisfaction
• Daily activities

Integrating these measures with clinical information may provide a more comprehensive picture of treatment impact.

 

Real-World Evidence and Cancer Treatment Effectiveness

One of the most important applications of RWE is understanding treatment effectiveness in routine clinical practice.

A treatment that performs well in a controlled clinical trial may encounter different circumstances when used across a much broader population.

Patients in routine care may have:

• Multiple comorbidities
• Different levels of organ function
• Previous treatments
• Different performance status
• Complex medication histories
• Less common disease characteristics
• Different levels of healthcare access

Real-world studies can therefore provide complementary information about how therapies perform across diverse populations.

The FDA's Oncology Real World Evidence Program focuses on rigorous methods for using RWD to generate RWE in oncology product development and patient-centered regulatory decision-making.

 

Real-World Evidence and Cancer Safety Monitoring

Safety monitoring is another important area where real-world data can contribute.

Clinical trials may involve relatively limited numbers of participants and carefully controlled conditions. After a therapy enters routine clinical practice, it may be used by substantially larger and more diverse populations.

Real-world data can help identify potential safety signals and characterize adverse events across broader groups.

Researchers can examine patterns involving:

• Adverse events
• Treatment discontinuation
• Hospitalization
• Medication combinations
• Long-term safety
• Rare complications

This does not mean that every association identified in RWD represents a causal relationship. Additional investigation and appropriate study design are necessary to interpret potential safety signals.

 

Real-World Evidence in Rare Cancers

Rare cancers can present particular challenges for traditional clinical research because recruiting sufficiently large patient populations may be difficult.

Real-world datasets can potentially bring together information from multiple institutions or healthcare systems, creating larger populations for observational research.

This can help researchers explore:

• Natural disease history
• Treatment patterns
• Clinical outcomes
• Patient characteristics
• Healthcare utilization
• Potential treatment options

For rare cancers, carefully designed RWE studies may therefore complement clinical trial evidence and contribute to a more complete understanding of patient care.

 

Real-World Evidence and Underrepresented Populations

One important potential advantage of RWE is its ability to capture patients who may be underrepresented in traditional clinical research.

Differences in healthcare access, geography, age, comorbidities, socioeconomic circumstances, and other factors can influence participation in clinical trials.

Real-world oncology data may help researchers investigate outcomes across broader populations.

However, RWD does not automatically eliminate health disparities.

If certain communities are poorly represented in the underlying healthcare data, the resulting evidence may still be incomplete.

Researchers therefore need to evaluate whether the data source adequately represents the population for which conclusions are intended.

The FDA specifically identifies diversity, health equity, and underrepresented populations as areas of interest within oncology RWD research.

 

Real-World Evidence in Precision Oncology

Precision oncology increasingly relies on molecular and genomic information to guide cancer treatment.

Real-world datasets can potentially connect molecular characteristics with treatment decisions and clinical outcomes.

Researchers may investigate questions such as:

• How frequently are patients receiving biomarker testing?
• Which genomic alterations are identified?
• How are targeted treatments used in routine practice?
• Do treatment patterns differ between healthcare systems?
• What outcomes are observed in specific molecular subgroups?

This creates an important connection between real-world evidence and personalized cancer care.

The FDA's oncology RWE research priorities include studying biomarker testing and the epidemiology of genomic subsets relevant to precision oncology.

 

Real-World Evidence and Immuno-Oncology

Immunotherapy has transformed treatment for several cancers, but its effectiveness and safety can vary across patient populations.

Real-world evidence can help researchers understand how immunotherapies are being used outside clinical trials.

Researchers may study:

• Treatment utilization
• Duration of therapy
• Treatment combinations
• Adverse events
• Response patterns
• Treatment discontinuation
• Outcomes across different patient populations

RWE may also help researchers investigate patient characteristics that were less extensively represented in clinical trials.

Importantly, observational findings need careful interpretation because treatment selection in routine care is influenced by many factors.

 

External Controls and Clinical Trial Research

Another important application of RWD is the development of external comparator groups.

In some research settings, investigators may use carefully selected real-world patients as a comparison population for a clinical study.

This approach can be particularly relevant when randomized trials are difficult to conduct or when studying rare diseases.

However, external control designs require rigorous methodology.

Researchers need to carefully consider:

• Patient eligibility
• Treatment exposure
• Disease severity
• Timing of treatment
• Outcome definitions
• Follow-up duration
• Confounding factors
• Missing data
• Differences between data sources

The FDA-supported ENCORE project is examining methods for comparing RWE studies with randomized oncology trials, including trial emulation using multiple EHR data sources.

This work highlights the importance of methodological rigor when using observational data to answer questions about treatment effectiveness.

 

Real-World Evidence in Regulatory Decision-Making

RWE is increasingly relevant to regulatory science.

The FDA has established an Oncology Real World Evidence Program to advance the appropriate use of RWD for generating evidence in oncology product development and regulatory decision-making.

FDA guidance also provides considerations for using electronic health records, medical claims data, and registries in studies intended to support regulatory decisions regarding medical products.

This does not mean that every real-world dataset can support a regulatory conclusion.

The quality, reliability, relevance, provenance, completeness, and analytical methodology of the data must be considered.

The key principle is therefore not simply more data, but fit-for-purpose data that can generate reliable evidence.

 

Artificial Intelligence and Real-World Oncology Data

Artificial intelligence and machine learning are creating new opportunities for analyzing large oncology datasets.

Modern healthcare systems can generate information at a scale that may be difficult to analyze using conventional methods alone.

AI may help researchers identify patterns across:

• Clinical records
• Imaging
• Laboratory results
• Genomic information
• Treatment histories
• Patient-reported outcomes
• Longitudinal healthcare data

Natural language processing may also help extract clinically relevant information from unstructured medical notes.

However, AI-generated findings are only as reliable as the underlying data and methodology.

Poor-quality data, incomplete records, systematic bias, and inappropriate model assumptions can produce misleading results.

Therefore, AI should be viewed as an analytical tool within a rigorous research framework rather than as a replacement for clinical and scientific expertise.

 

Data Quality: One of the Biggest Challenges

The usefulness of RWE depends heavily on data quality.

Real-world healthcare data is not always collected specifically for research purposes.

Potential problems include:

• Missing information
• Inconsistent coding
• Duplicate records
• Incomplete follow-up
• Differences between institutions
• Incorrect data entry
• Changing treatment practices
• Inconsistent outcome definitions
• Limited information about certain clinical variables

NCI and FDA initiatives emphasize improving data quality, standardization, interoperability, provenance, and fitness for purpose in oncology RWD research.

Researchers must therefore evaluate the data source before drawing conclusions.

 

Bias and Confounding in Real-World Evidence

Unlike randomized clinical trials, observational real-world studies generally do not randomly assign treatments.

This creates the possibility of confounding.

For example, patients receiving one treatment may differ systematically from patients receiving another treatment because of disease severity, age, comorbidities, previous therapy, or other factors.

These differences can influence outcomes independently of the treatment itself.

Researchers use statistical and epidemiological methods to reduce the impact of confounding and other sources of bias.

Appropriate study design, transparent definitions, sensitivity analyses, and careful interpretation are essential.

The FDA's oncology RWE research priorities specifically include evaluating bias, confounding, data quality, and other threats to study validity.

 

Privacy, Security, and Ethical Considerations

Cancer datasets can contain highly sensitive health information.

As RWD research expands, protecting patient privacy becomes increasingly important.

Organizations working with oncology data need appropriate approaches to:

• Data security
• De-identification
• Access control
• Data governance
• Consent
• Ethical research practices
• Secure data sharing
• Responsible secondary data use

Researchers must balance the scientific value of data with the rights and expectations of patients.

Trust is essential for the development of sustainable real-world evidence ecosystems.

 

Interoperability and Data Standardization

Another major challenge is that healthcare data is often stored in different formats across hospitals, laboratories, registries, and other systems.

A patient's information may be distributed across multiple institutions and databases.

Without appropriate standards, linking these datasets can be difficult.

Improving interoperability can help researchers create more comprehensive longitudinal datasets while reducing duplication and inconsistency.

NCI's work through the USCDI+ Cancer initiative includes efforts to define cancer research-specific data concepts that can support data exchange and harmonization across institutions.

Standardization may become increasingly important as oncology research becomes more dependent on integrated clinical, molecular, imaging, and patient-generated information.

 

Real-World Evidence and Clinical Decision-Making

RWE may also contribute to clinical decision-making by providing information about how treatments perform in everyday practice.

Clinicians could potentially use evidence from large patient populations to better understand treatment patterns and outcomes for patients with similar characteristics.

However, real-world evidence should complement clinical expertise, published clinical evidence, guidelines, and individual patient considerations.

A treatment decision should never be based solely on an observational dataset.

Instead, RWE can become one component of a broader evidence framework.

 

Building a Learning Healthcare System

One of the most promising long-term opportunities is the development of a learning healthcare system.

In such a model, information generated during routine care can contribute to research, while research findings can subsequently improve clinical practice.

This creates a continuous cycle:

Clinical Care → Data Collection → Analysis → Evidence → Improved Care

For oncology, this approach could help healthcare systems learn more rapidly from treatment experiences across large patient populations.

It could also support continuous evaluation of emerging therapies and changing treatment practices.

 

The Future of Real-World Evidence in Oncology

The future of oncology RWE is likely to involve increasingly integrated datasets.

Clinical information may be combined with:

• Genomic data
• Imaging data
• Pathology
• Treatment records
• Patient-reported outcomes
• Wearable-device data
• Digital health information
• Population-level datasets

Advanced analytics and AI may help researchers identify relationships within these complex datasets.

At the same time, methodological standards will need to continue evolving.

The future challenge is not simply collecting larger quantities of data. It is determining which data are relevant, reliable, representative, and sufficiently complete to answer specific clinical and regulatory questions.

The FDA's current oncology RWE work emphasizes rigorous methodological development, collaboration, data quality, and appropriate application of RWD.

 

Key Benefits of Real-World Evidence in Cancer Care

When appropriately designed and analyzed, RWE can provide several important benefits.

1. Broader Patient Populations

RWE can capture patients treated in routine practice, including populations that may be less represented in clinical trials.

2. Longer-Term Insights

Real-world datasets may provide longitudinal information that helps researchers examine treatment patterns and outcomes over extended periods.

3. Understanding Routine Practice

RWE can show how treatments are actually used across different healthcare settings.

4. Safety Monitoring

Large datasets can help researchers identify potential safety signals and characterize adverse events.

5. Treatment Pattern Analysis

RWE can reveal how therapies are initiated, modified, combined, or discontinued.

6. Health Equity Research

Real-world datasets can help investigate differences in treatment and outcomes across patient populations when the underlying data adequately represents those groups.

7. Support for Regulatory Science

When appropriately designed and validated, RWE can contribute to regulatory evidence generation.

 

What Real-World Evidence Cannot Do Alone

Despite its growing importance, RWE has limitations.

Real-world evidence should not automatically be interpreted as proof that one treatment causes a particular outcome.

Observational datasets may contain confounding, selection bias, missing data, measurement differences, and other limitations.

Randomized clinical trials remain an essential method for evaluating causal treatment effects.

The strongest future evidence generation strategy may therefore involve combining complementary approaches rather than treating RCTs and RWE as competing methodologies.

Clinical trials can provide controlled estimates of efficacy and safety, while RWE can help demonstrate how treatments perform across broader and more diverse real-world settings.

 

Real-World Evidence and the Evolution of Cancer Research

Cancer research is increasingly moving toward a more connected evidence ecosystem.

Instead of relying on isolated clinical trials or individual datasets, researchers can increasingly integrate evidence from multiple sources.

This evolution can support a broader understanding of:

Who receives treatment → Which treatment is used → How treatment is delivered → How patients respond → What outcomes occur → How care can improve

Real-world evidence is an important component of this transition.

As data systems become more interoperable and analytical methods become more sophisticated, oncology researchers may be able to answer increasingly complex questions about treatment effectiveness, safety, access, outcomes, and patient experience.

 

Conclusion

Real-world evidence in oncology is transforming how researchers, clinicians, healthcare organizations, and regulators understand cancer care beyond the traditional clinical trial environment.

Real-world data from electronic health records, cancer registries, medical claims, patient-reported outcomes, digital technologies, and other sources can provide valuable information about patients receiving care in routine practice. When these data are carefully evaluated and analyzed using appropriate methodologies, they can generate evidence that complements clinical trial findings.

The growing importance of RWE does not signal the end of randomized clinical research. Instead, it represents an opportunity to build a more comprehensive evidence ecosystem in which controlled clinical trials and real-world studies answer complementary questions.

The future of oncology will likely depend on the ability to connect high-quality data with rigorous research methods, responsible data governance, advanced analytics, and patient-centered clinical decision-making.

As cancer care becomes increasingly complex and personalized, real-world evidence may help bridge the gap between what is demonstrated in research settings and what happens in everyday clinical practice.

Ultimately, the goal is not simply to collect more cancer data. It is to transform meaningful real-world information into reliable evidence that can support better research, better decisions, and better cancer care.

 

About WCOCC-2026

The World Conference on Oncology & Cancer Care (WCOCC-2026) brings together researchers, oncologists, clinicians, scientists, healthcare professionals, and experts from across the oncology community to discuss emerging developments in cancer research and care.

📅 November 19–21, 2026
📍 Tokyo, Japan

The conference provides an international platform for sharing research, discussing emerging technologies, exchanging clinical perspectives, and exploring innovations shaping the future of oncology and cancer care.
FAQs

1. What is real-world evidence in oncology?

Real-world evidence (RWE) in oncology is clinical evidence generated by analyzing real-world data (RWD) collected during routine healthcare. It can provide insights into treatment effectiveness, safety, patient outcomes, and healthcare practices outside traditional clinical trial settings.

2. What is real-world data in oncology?

Real-world data refers to routinely collected information about patients and healthcare delivery. Sources can include electronic health records, medical claims, cancer registries, patient-reported data, and other health data sources.

3. How is RWE different from clinical trial evidence?

Clinical trials are designed under controlled research conditions, while RWE is generated from data reflecting routine clinical practice. RWE can complement clinical trial findings by providing information about how treatments perform across broader and more diverse patient populations.

4. How can real-world evidence improve cancer treatment?

RWE can help researchers and healthcare professionals understand treatment patterns, effectiveness, safety, patient outcomes, and how therapies are used in routine cancer care. This can support more informed evidence-based treatment decisions.

5. What are the major sources of real-world data in oncology?

Important sources include electronic health records, medical claims, cancer and disease registries, patient-reported outcomes, and other routinely collected healthcare or digital health data.

6. Can real-world evidence support cancer drug development?

Yes. RWE is increasingly being studied and used in oncology drug development and regulatory science. The FDA's Oncology Real World Evidence Program focuses on rigorous approaches for using RWD to generate RWE in oncology product development and regulatory decision-making.

7. What are the challenges of using real-world data in oncology?

Key challenges include missing or inconsistent data, data quality, interoperability, bias, confounding, differences between data sources, privacy concerns, and difficulties in accurately defining clinical outcomes.

8. How can artificial intelligence improve real-world evidence research?

Artificial intelligence and machine learning can help researchers process large and complex datasets, identify patterns, extract information from clinical records, and support analysis of clinical, genomic, imaging, and other healthcare data.

9. Can RWE help with personalized cancer care?

Yes. By analyzing treatment outcomes and patient characteristics across real-world populations, RWE can contribute to understanding which treatments may work best for particular patient groups and support the development of more personalized approaches to cancer care.

10. What is the future of real-world evidence in oncology?

The future is likely to involve greater integration of electronic health records, registries, genomic information, imaging, patient-reported outcomes, AI, and standardized data systems. Improving data quality and methodological rigor will be essential for generating reliable oncology RWE.