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PP41 Cost-Effectiveness Modeling Of Chimeric Antigen Receptor T-Cell Therapies

Published online by Cambridge University Press:  31 December 2019

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Abstract

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Introduction

This study has two key aims. The first is to review cost-effectiveness (CE) models for chimeric antigen receptor T-cell (CAR-T) therapies that have been appraised by health technology assessment (HTA) authorities. The second is to identify the key challenges of CE modeling of CAR-T therapies based on the main points raised in the HTA appraisals.

Methods

A targeted HTA review of published CE models for CAR-T therapies in the United Kingdom (UK) and United States (US) was undertaken.

Results

Four relevant CE models were identified – three from the UK and one in the US. Of the three UK models, two were single technology submissions to the National Institute for Health and Care Excellence (NICE) and one was a ‘mock’ appraisal undertaken by NICE with a hypothetical evidence dataset. The one US model was published by the Institute for Clinical and Economic Review (ICER) committee. Two key model structures were adopted across the appraisals: a three-health state partitioned survival analysis model and a short-term decision tree followed by a three-health state partitioned survival model. The key modeling challenges identified can by summarized into five main categories: comparator evidence generation, estimation of long-term survival, curative benefit, health-related quality of life, and infrastructure/training requirements.

Conclusions

There are many challenges associated with the CE modeling of CAR-T therapies, with the most critical issues related to how uncertainty for long-term efficacy and safety can be addressed and mitigated. With more mature evidence sets in the future, stakeholders will get a clearer picture for the long-term benefit and risk of CAR-T therapies, but until then it is likely that HTA authorities will take a conservative stand when appraising the comparative value of CAR-T therapies.

Type
Poster Presentations
Copyright
Copyright © Cambridge University Press 2019