PURPOSE: To discuss how treatment effect waning is dealt with when modelling the cost-effectiveness of novel immune-oncology therapies in health technology assessments (HTAs) and provide recommendations regarding good practices.

DESCRIPTION: Novel immuno-oncology therapies are demonstrating durable responses, extending survival across multiple cancer types by stimulating the body’s own immune system.

• With longer term data, survival plateaus have been observed, although the evidence generation to demonstrate long-term effects is ongoing.
• In HTAs, the cost-effectiveness assessment of these therapies requires modelling their lifetime effect on patients, usually based on immature trial data.
• HTA agencies commonly request alternative hypothetical scenarios on the duration and size of the treatment effect over time, as these are key cost-effectiveness drivers.
• Since methodological consensus is lacking, manufacturers and assessment groups have frequently adopted non-evidence-based, arbitrary assumptions regarding treatment effect waning, while HTA agencies have considered conservative approaches in decision making.

• Raquel Aguiar-Ibáñez will present current HTA requirements regarding treatment effect waning, and how manufacturers and HTA agencies have dealt with uncertainty around immuno-oncology therapies’ long-term effectiveness.
• Professor Gianluca Baio will discuss the role of supportive datasets and the advantages of adopting a Bayesian framework to account for the use of real-world evidence and clinical expert judgement to better reflect expected long-term benefits.
• Dawn Lee will focus on how uncertainty around long-term treatment effects might be incorporated into economic analyses depending on the modelling framework (e.g. for partitioned survival versus state transition models) and the potential to collect additional data via managed access arrangements.
• Nick Latimer will provide a decision-maker’s perspective on types of evidence that may be used to support long-term efficacy assumptions.