Economic Evaluation of Single versus Combination Immuno-Oncology Therapies: Application of a Novel Modelling Approach in Metastatic Melanoma
Received 15 November 2019
Accepted for publication 26 March 2020
Published 6 May 2020 Volume 2020:12 Pages 241—252
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Dean Smith
Eddie J Gibson,1 Najida Begum,1 Ian Koblbauer,1 George Dranitsaris,2 Danny Liew,3 Phil McEwan,4 Yong Yuan,5 Ariadna Juarez-Garcia,6 David Tyas,6 Clive Pritchard1
1Wickenstones Ltd, Abingdon, UK; 2Augmentium Pharma Consulting Inc., Toronto, ON, Canada; 3School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia; 4Health Economics and Outcomes Research Ltd, Cardiff, UK; 5Bristol-Myers Squibb, Lawrenceville, NJ, USA; 6Bristol-Myers Squibb, Uxbridge, UK
Correspondence: Clive Pritchard
Wickenstones Ltd., Units 24-26, 127 Olympic Avenue, Milton Park, Abingdon, Oxfordshire OX14 4SA, UK
Tel + 441865 959735
Background: Existing economic model frameworks may not adequately capture the atypical treatment response patterns in immuno-oncology (I-O) compared with conventional therapies and thus may fail to represent the full clinical value associated with disease dynamics and improved survival.
Objective: A cost-effectiveness analysis (CEA) of the I-O Regimen (nivolumab/ipilimumab) versus ipilimumab alone in advanced melanoma was carried out by applying a 5-state partitioned survival model (PSM) as a case study, to explore the I-O treatment response and clinical outcomes. The findings were compared with those of a conventional 3-state PSM.
Materials and Methods: The case study extends the conventional 3-state PSM, by separating the pre-progression state into non-responders and responders, and the post-progression state into normal and I-O progression to account for delayed treatment effects preceding clinical response. Model states were populated using patient-level data (where possible), mapping from the best overall response (BOR), and survival analysis with flexible and traditional parametric methods. Survival functions were applied to progression-free survival (PFS) and overall survival (OS) endpoints across treatment arms using the 4-year follow-up data (data available at the time of the research; since then 5-year follow-up data have been published) from the CheckMate 067 trial. Information on BOR was used as a means of differentiating the I-O treatment response in addition to the outcomes of progression-free and progressed disease. A UK National Health Service and personal social services (NHS/PSS) perspective over a lifetime horizon was used with outcomes discounted at 3.5% annually.
Results: The 5-state PSM generated an increase in quality adjusted life years (QALYs) in both treatment arms and gave a more granular description of patients’ health profiles compared with the traditional 3-state PSM. The incremental QALY increased by 13% (from 2.62 to 2.95 QALYs) and the incremental cost decreased by 12% (£ 29,125 to £ 25,678) with the 5-state model. In both models, the Regimen had an incremental cost-effectiveness ratio (ICER) relative to ipilimumab alone within the lower bound of the National Institute for Health and Care Excellence (NICE) reference range (£ 20,000 per QALY gained).
Conclusion: A 5-state economic model, incorporating relevant I-O health states, can be more informative to gain insight into treatment response and progression differences that are not commonly captured in existing economic models. Clinical trial endpoints, including those relating to treatment response, which are not directly reported in ongoing I-O trials, can be mapped on to the proposed modelled health states (although assumptions are required to do so). Improvements in reporting treatment response in future I-O clinical trials could help to further validate and improve the proposed model framework.
Keywords: immunotherapy, cost-effectiveness, melanoma, CheckMate 067
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