Article type
Abstract
Background
Establishing explicit decision thresholds enables a clear and systematic evaluation of the certainty of evidence and facilitate a consistent decision-making. Ideally, these thresholds should be grounded in empirical research or in broadly recognized, transparently achieved consensus. The European Society of Medical Oncology - Magnitude of Clinical Benefit Scale (ESMO-MCBS) is an instrument designed to quantify the clinical benefit of anti-cancer medications. Until recently, it had not been applied in the GRADE framework due to perceived incompatibilities.
Objective
To describe the integration of the ESMO-MCBS with the GRADE Evidence to Decision framework and its use in coverage decisions of anti-cancer medications.
Methods
The ESMO-MCBS was adapted to establish decision thresholds categorizing the benefits and harms of anti-cancer medications into four levels: trivial, small, moderate, or large. These thresholds were subsequently utilized by a guideline panel to assess the certainty of evidence using the GRADE approach and to develop coverage recommendations using the Evidence to Decision (EtD) framework.
Results
Between November 2023 and January 2024, a guideline panel comprising 21 clinical experts and 4 methodologists developed coverage decisions for 21 anti-cancer medications. Out of the 21 interventions assessed, the panel recommended coverage for 19. Implementing decision thresholds based on the ESMO-MCBS and the GRADE approach improved the decision-making process. Moreover, the magnitude of benefits determined by these thresholds were the primary predictor of the recommendation's direction.
Conclusion:
This study describes a novel integration of a well-established consensus and the GRADE approach to define thresholds for the magnitude of benefits and harms of anti-cancer medications. Our findings demonstrate that explicit thresholds significantly improve the transparency and consistency of the decision-making process. This innovative methodology offers a replicable model for future coverage decisions and guideline development.
Establishing explicit decision thresholds enables a clear and systematic evaluation of the certainty of evidence and facilitate a consistent decision-making. Ideally, these thresholds should be grounded in empirical research or in broadly recognized, transparently achieved consensus. The European Society of Medical Oncology - Magnitude of Clinical Benefit Scale (ESMO-MCBS) is an instrument designed to quantify the clinical benefit of anti-cancer medications. Until recently, it had not been applied in the GRADE framework due to perceived incompatibilities.
Objective
To describe the integration of the ESMO-MCBS with the GRADE Evidence to Decision framework and its use in coverage decisions of anti-cancer medications.
Methods
The ESMO-MCBS was adapted to establish decision thresholds categorizing the benefits and harms of anti-cancer medications into four levels: trivial, small, moderate, or large. These thresholds were subsequently utilized by a guideline panel to assess the certainty of evidence using the GRADE approach and to develop coverage recommendations using the Evidence to Decision (EtD) framework.
Results
Between November 2023 and January 2024, a guideline panel comprising 21 clinical experts and 4 methodologists developed coverage decisions for 21 anti-cancer medications. Out of the 21 interventions assessed, the panel recommended coverage for 19. Implementing decision thresholds based on the ESMO-MCBS and the GRADE approach improved the decision-making process. Moreover, the magnitude of benefits determined by these thresholds were the primary predictor of the recommendation's direction.
Conclusion:
This study describes a novel integration of a well-established consensus and the GRADE approach to define thresholds for the magnitude of benefits and harms of anti-cancer medications. Our findings demonstrate that explicit thresholds significantly improve the transparency and consistency of the decision-making process. This innovative methodology offers a replicable model for future coverage decisions and guideline development.