Real-World Data Proves Reliable for Lung Cancer Research

Clinical trials traditionally rely on randomisation, pitting a new treatment against a standard control group to measure efficacy. However, single-arm trials lack this direct comparison, forcing researchers to look elsewhere for context. A new meta-analysis confirms that ‘External Controls’ derived from real-world data (RWD)—such as electronic health records—can effectively bridge this gap in non-small cell lung cancer research.

By comparing 14 chemotherapy control arms from diverse clinical trials against data from the Flatiron Health database, researchers assessed progression-free survival (PFS). This metric measures how long a patient lives with the disease without it getting worse. The results were striking: real-world outcomes mirrored clinical trial results almost perfectly, with a mean difference near zero, barring one outlier.

While the alignment increases confidence in using medical records to contextualise new treatments, the study warns of ‘between-study variation’. To avoid incorrect decision-making, statistical models must adjust for potential biases inherent in non-randomised data. Ultimately, this validates the use of RWD as a powerful tool for accelerating drug development when traditional control groups are unavailable.