ASCO Value Framework and ESMO-Magnitude of Clinical Benefit Scale in the Incorporation of New Technologies: Case Study and Challenges in the Private Health Care System

OBJECTIVES: The rapid advancement of oncological technologies, combined with the trend of approving new cancer drugs based on trials with immature survival data, underscores the need for robust, evidence-based decision-making strategies. The ASCO framework and ESMO-MCBS are validated tools designed to assess clinical benefit in oncology. This study aims to evaluate the utility and limitations of these tools in integrating new technologies in the institutional protocol of an Oncology Network in Brazil.

METHODS: We applied the ASCO and ESMO tools to evaluate the drugs approved by the Brazilian regulatory agency (ANVISA) for solid tumors in the last two-years. We considered the data reported in the clinical trials. Additionally, we assessed the risk of bias using the ROB-2 tool.

RESULTS: Our analysis included 27 approvals listed on the ANVISA website. Among these, three (11.1%) were Phase-I/II trials, and 24 (88.9%) were Phase-III, with one non-randomized. Eight trials (29.6%) were blinded, while 19 (70.4%) were open-label, leading to a high risk of bias in the randomization process. Eleven trials (40.7%) listed overall survival as the primary endpoint. The mean ASCO score was 49.9±16.9, with 14 trials (53.8%) meeting the cutoff score of 45 for relevant benefit. For ESMO-MCBS, all four trials in the curative setting achieved a top “A” rate while among non-curative therapies, 15 of 23 trials (65.2%) scored grade 4 or 5 (relevant benefit). Notably, there was a 45% disagreement between ASCO and ESMO frameworks. Less than 50% of trials were well rated by both frameworks and this rate fells to 11% when the risk of bias was considered.

CONCLUSIONS: The low rate of studies scoring high grade at clinical benefit frameworks highlights significant concerns and illustrates the challenges of incorporating new technologies. The disparity in results is driving us to develop new tools to ensure confidence in data interpretation.