Cost, Context, and Confounding: Rethinking Real-World data Generation and Interpretation in Advanced NSCLC.
Mehmet Mutlu Çatlı1, Arif Hakan Önder1
1Department of Medical Oncology, Antalya Training and Research Hospital, Antalya, Turkey.
The Oncologist
|May 4, 2026
Summary
Real-world data (RWD) can guide immunotherapy decisions in non-small cell lung cancer (NSCLC) but requires careful interpretation. Methodological rigor is essential to avoid biased economic models and ensure strategic policy application.
Area of Science:
- Oncology
- Health Economics
- Real-World Data Analysis
Background:
- Immunotherapy is standard for advanced NSCLC, creating uncertainty in post-progression treatment choices.
- A study suggested pembrolizumab rechallenge benefits patients after chemo-immunotherapy progression.
- This raises questions on interpreting and applying real-world data (RWD) in health economics.
Purpose of the Study:
- Critically analyze assumptions in retrospective RWD-based cost-effectiveness modeling.
- Examine how RWD biases can influence conclusions on immunotherapy rechallenge value.
- Propose a framework to differentiate clinical signals from policy-grade evidence.
Main Methods:
- Retrospective analysis of RWD.
- Critical appraisal of cost-effectiveness modeling methodologies.
- Case study using Velcheti et al. findings as an anchor.
Main Results:
- Unadjusted survival metrics in RWD may inflate cost-effectiveness estimates (ICER/QALY).
- Structural biases in RWD can lead to premature conclusions about immunotherapy rechallenge.
- A proposed framework aids in distinguishing clinical signals from policy-grade evidence.
Conclusions:
- Greater rigor is needed when translating RWD into economic models for oncology.
- Analytic transparency, sensitivity analyses, and patient-level adjustments are crucial for RWD in value-based care.
- RWD are hypothesis generators, not replacements for controlled evidence; methodological discipline is key for strategic policy application.
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