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The impact of increased post-progression survival on the cost-effectiveness of interventions in oncology

Authors Retzler J, Davies H, Jenks M, Kiff C, Taylor M

Received 18 October 2018

Accepted for publication 8 March 2019

Published 3 May 2019 Volume 2019:11 Pages 309—324


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Giorgio Lorenzo Colombo

Jenny Retzler,1,2 Heather Davies,2 Michelle Jenks,2 Christopher Kiff,3 Matthew Taylor2

1Department of Psychology, University of Huddersfield, Huddersfield, UK; 2York Health Economics Consortium, University of York, York, UK; 3Bristol-Myers Squibb Pharmaceuticals Ltd, Uxbridge, UK

Purpose: Cost-effectiveness analyses (CEA) of new technologies typically include “background” costs (eg, all “related” health care costs other than the specific technology under evaluation) as well as drug costs. In oncology, these are often expensive. The marginal cost-effectiveness ratio (ie, the extra costs and QALYs associated with each extra period of survival) calculates the ratio of background costs to QALYs during post-progression. With high background costs, the incremental cost-effectiveness ratio (ICER) can become less favorable as survival increases and the ICER moves closer to the marginal cost-effectiveness ratio, making cost-effectiveness prohibitive. This study assessed different methods to determine whether high ICERs are caused by high drug costs, high “background costs” or a combination of both and how different approaches can alter the impact of background costs on the ICER where the marginal cost-effectiveness ratio is close to, or above, the cost-effectiveness threshold.
Methods: The National Institute for Health and Care Excellence oncology technology appraisals published or updated between October 2012 and October 2017 were reviewed. A case study was selected, and the CEA was replicated. Three modeling approaches were tested on the case study model.
Results: Applying one-off “transition” costs during post-progression reduced the ongoing “incremental” costs of survival, which meant that the marginal cost-effectiveness ratio was substantially reduced and problems associated with additional survival were less likely to impact the ICER. Similarly, the use of two methods of additional utility weighting for end-of-life cases meant that the marginal cost-effectiveness ratio was reduced proportionally, again lessening the impact of increased survival.
Conclusion: High ICERs can be caused by factors other than the cost of the drug being assessed. The economic models should be correct and valid, reflecting the true nature of marginal survival. Further research is needed to assess how alternative approaches to the measurement and application of background costs and benefits may provide an accurate assessment of the incremental benefits of life-extending oncology drugs. If marginal survival costs are incorrectly calculated (ie, by summing total post-progressed costs and dividing by the number of baseline months in that state), then the costs of marginal survival are likely to be overstated in economic models.

Keywords: cancer, cost, economics, overall survival, quality of life, modeling

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