Analyzing the clinical benefit of newer therapies for advanced or metastatic non-small-cell lung cancer: application of the ESMO-magnitude of clinical benefit scale v1.1

Acta Oncol. 2021 Jun 29:1-8. doi: 10.1080/0284186X.2021.1942546. Online ahead of print.


BACKGROUND: Despite newer therapies, advanced or metastatic non-small-cell lung cancer (NSCLC) continues to be the leading cause of cancer-related deaths worldwide. Deficits in the design and methods of randomized controlled trials (RCTs) may contribute to reducing the clinical benefit of therapies in oncology. To prioritize treatments based on efficacy results and toxicity data, the European Society for Medical Oncology (ESMO) has developed the Magnitude of Clinical Benefit Scale (MCBS). The objective of this study was to apply the ESMO-MCBS v1.1 to a cohort of RCTs on therapies for advanced or metastatic NSCLC.

MATERIAL AND METHODS: Phase III and pivotal phase II trials, published between 2013 and 2018, investigating drug therapies for advanced NSCLC were included. PubMed was specifically searched for efficacy/toxicity updates. Treatments were graded 5 to 1 on the ESMO-MCBS v1.1, using the lower limit of the 95% confidence interval of the hazard ratio (HR), where scores 5 and 4 represent a substantial clinical benefit. Additionally, scores using the point estimate HR were generated, for comparison. Discrepancies between our grade estimations and the ones published on the ESMO website, as scorecards, were identified.

RESULTS: ESMO-MCBS scores were calculated for 42 positive clinical trials. 54.8% met the ESMO-MCBS thresholds for clinically meaningful benefit (final grade of 4 or 5). That percentage decreased to 40.5% when considering the point estimate of the HR. 50.0% of the trials had no published scorecard on the ESMO website and discrepancies affected 11 (26.2%) studies.

CONCLUSION: Almost half of the RCTs showing a statistically significant result favoring the experimental arm, failed to demonstrate a substantial clinical benefit according to the ESMO framework.

PMID:34184595 | DOI:10.1080/0284186X.2021.1942546