Understanding long-term patient outcomes and quality of life using real-world data in oncology

Executive summary

Long-term outcomes and quality of life are now central to demonstrating oncology value, but they cannot be understood through clinical trials or fragmented real-world data alone.

Clinical trials provide evidence of efficacy under controlled conditions, but they are not designed to capture how treatments perform across diverse patient populations, evolving treatment pathways, and extended follow-up periods in routine care.

This challenge is particularly pronounced in oncology. Treatment decisions increasingly depend on biomarker-defined populations, and patients often move through multiple treatments and lines of therapy. Teams must assess outcomes such as progression, survival, and quality of life over time.

For market access, HEOR, and evidence-generation teams, the goal is not simply to answer one question. It is to build a reusable, longitudinal evidence foundation that follows defined patient populations over time, connects clinical, biomarker, imaging, laboratory, and patient-experience data, and supports reimbursement, HTA, positioning, and lifecycle decisions across markets.

What’s needed post-launch

For oncology teams responsible for market access and evidence generation, the challenge is not only showing how treatments perform over time in routine clinical practice. It is generating evidence that is consistent, comparable, and decision-ready enough to support reimbursement, positioning, and lifecycle decisions across markets.

However, after launch, stakeholders begin asking different questions: Does the treatment benefit persist beyond clinical trial follow-up? How do outcomes evolve across lines of therapy? How does the treatment compare with current standards of care? What is the long-term impact on patient quality of life?

Answering these questions requires evidence that can:

• Reflect real patient populations, including disease stage, comorbidities, and biomarker status
• Track outcomes over time across lines of therapy and care settings
• Capture clinically relevant endpoints such as progression-free survival, overall survival, and quality of life
• Remain consistent and comparable across analyses and markets

Together, these insights support reimbursement, HTA, and payer discussions, product positioning, and lifecycle strategy decisions.

Why this is difficult in practice

Traditional data sources provide only part of the evidence needed to understand long-term oncology outcomes.

• Claims data provide scale and visibility into healthcare utilization, but often lack the clinical depth, outcome specificity, and timeliness needed to assess progression, treatment response, biomarker-defined populations, and quality of life.
• Electronic health records provide rich clinical detail but often reflect care delivered within a single institution.
• Imaging, laboratory, and biomarker data are essential for understanding disease progression and treatment response, yet they are often stored separately and can be difficult to link at the patient level.
• Patient-reported outcomes (PROs) offer important insight into symptoms, treatment burden, and quality of life, but they are not collected consistently or routinely linked to clinical events.
• Outcomes data can be difficult to compare because clinically meaningful endpoints such as progression, response, survival, treatment discontinuation, adverse events, and quality of life are often defined and recorded differently across data sources and care settings.

Together, these limitations make it difficult to define patient populations consistently, follow patients over time, and compare outcomes across studies, markets, or geographies.

Oncology-specific evidence challenges

At the same time, oncology care itself creates evidence challenges that static or one-time analyses cannot fully address:

Treatment pathways

Patients move across treatment lines, combination therapies, and evolving standards of care.

Response and outcomes assessment

Response, progression, recurrence, and other outcomes require ongoing assessment and may vary based on treatment sequence, biomarker status, comorbidities, and care setting.

Long-term follow-up

Survival, recurrence, late adverse events, and quality-of-life outcomes often require extended follow-up.

Precision oncology

Evidence depends on identifying biomarker-defined patients, linking testing and treatment data, and tracking outcomes over time.

Long-term outcomes, quality of life, and durability of response are key considerations in reimbursement, HTA, and lifecycle decision-making. Without robust longitudinal evidence, teams risk:

• Limited visibility into durability of response
• Weak or incomplete quality-of-life evidence
• Inconsistent results across geographies
• Repeated analyses that cannot be compared or reused
• Challenges defending evidence during HTA or payer review

As a result, organizations may struggle to demonstrate value consistently, differentiate from evolving standards of care, and defend evidence with confidence during reimbursement, HTA, and lifecycle strategy discussions across markets.

Where oncology evidence gaps emerge

These challenges typically emerge in four areas: defining the right patient population, following patients over time, measuring outcomes consistently, and integrating data across sources.

Incomplete patient population definition

Defining oncology cohorts requires detailed clinical attributes such as:

• Disease stage
• Biomarker status
• Prior lines of therapy

These variables are not consistently available across datasets, making it difficult to define comparable patient populations. Teams often recreate cohorts across sources, leading to variation in inclusion criteria and reduced consistency across analyses.

Limited longitudinal visibility across care settings

Cancer patients often receive care across multiple providers, institutions, and settings, which means most datasets capture only part of the patient journey:

• EHRs provide clinical depth but often stay within one institution
• Claims data provide breadth but often lack clinical detail and timeliness

Consequently, teams may miss key events such as treatment changes, progression, follow-up care, or outcomes recorded outside the originating institution.

Incomplete capture of outcomes and quality of life

Key endpoints require repeated assessment over time:

• Progression (via imaging)
• Survival
• Treatment tolerance and patient burden

These are often:

• Captured inconsistently
• Stored in unstructured formats
• Missing across datasets

In addition, quality of life is particularly difficult to assess longitudinally because patient-reported outcomes are not collected consistently, structured uniformly, or linked reliably to clinical events over time.

Fragmented data across modalities

Oncology evidence depends on multiple data types:

• Clinical records
• Imaging
• Laboratory data
• Biomarker and genomic data

When these data types are not integrated at the patient level, it becomes difficult to connect treatment decisions, outcomes, and patient experience in a way that can support decision-making.

What teams need to generate reusable oncology evidence

To generate post-launch oncology evidence, teams need a consistent longitudinal data foundation. It must connect defined patient populations, treatment journeys, and outcomes over time.

Core requirements

A consistent patient population

Defined once using clinically relevant variables and reused across analyses.

Longitudinal tracking across care settings

Patients followed over time, across providers and treatment lines.

Integrated outcome and quality-of-life data

Clinical endpoints and patient experience linked at the patient level.

Comparable and traceable evidence

Standardized definitions and transparent methods across analyses.

Together, these requirements define what is needed to generate regulatory‑grade real‑world evidence that can support filings, submissions, and external review with confidence.

Building longitudinal oncology evidence with BC Platforms

BC Platforms helps oncology teams turn fragmented real-world data into longitudinal evidence that can support market access, HEOR, and lifecycle decisions.

Through our global data partner network, teams can access clinically rich oncology data from routine care settings, including disease stage, biomarker status, treatment history, laboratory results, imaging, outcomes, and genomics where available. This makes it possible to define patient cohorts that reflect real-world clinical practice rather than relying on a single data source or one-off analysis.

Meanwhile, BC Catalyst enables teams to explore patient cohorts and biomarker-defined populations to investigate treatment pathways, sequencing, progression, survival outcomes, and other clinically relevant endpoints over time.

In addition, BC Unify harmonizes and integrates structured and unstructured data across clinical, imaging, laboratory, and biomarker sources. Teams can then define cohorts and endpoints consistently across countries and studies. That consistency gives teams a reusable evidence foundation for comparing outcomes, following treatment pathways, and adapting analyses as standards of care evolve.

Finally, BC Mosaic, our Trusted Research Environment, provides a secure workspace for accessing and analyzing de-identified patient-level data with the governance, traceability, and compliance needed for cross-market collaboration.

When teams need additional support, our consulting team can help shape study design, cohort construction, endpoint definition, and the broader evidence-generation approach so analyses stay aligned with scientific, HTA, payer, market access, and lifecycle objectives.

Conclusion

The strongest post-launch oncology evidence does more than describe what happened after treatment. It connects who patients are, how they move through care, the outcomes they experience, and how those outcomes evolve over time.

Market access, HEOR, and evidence-generation teams need a longitudinal evidence foundation. It must be consistent, reusable, and decision-ready across markets. Teams should define patient populations once and follow them over time, with clinical, biomarker, imaging, laboratory, and quality-of-life data connected at the patient level.This is where real-world data becomes strategically valuable: not as a one-off supplement to trial evidence, but as the infrastructure for demonstrating durable treatment value throughout the product lifecycle.

The real value of longitudinal real-world data is not a single analysis. It lies in the ability to build a reusable evidence foundation that follows defined oncology populations, treatment pathways, and outcomes over time.