An Investigation into the Relationship Between Choice of Model Structure and How to Adjust for Subsequent Therapies Using a Case Study in Oncology

Holly L. Cranmer (), Gemma E. Shields and Ash Bullement
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Holly L. Cranmer: Takeda UK
Gemma E. Shields: University of Manchester
Ash Bullement: Delta Hat

Applied Health Economics and Health Policy, 2023, vol. 21, issue 3, No 4, 385-394

Abstract: Abstract Background A common challenge in health technology assessments (HTAs) of cancer treatments is how subsequent therapy use within the trial follow-up may influence cost-effectiveness model outcomes. Although overall survival (OS) is often a key driver of model results, there are no guidelines to advise how to adjust for this potential confounding, with different approaches available dependent on the model structure. Objective We compared a partitioned survival analysis (PartSA) with a semi-Markov multi-state model (MSM) structure, with and without attempts to adjust for the impact of subsequent therapies on OS using a case study describing outcomes for people with relapsed/refractory multiple myeloma. Methods Both model structures included three health states: pre-progression, progressed disease and death. Three traditional crossover methods were considered within the context of the PartSA, whereas for the MSM, the probability of post-progression death was pooled across arms. Impacts on the model incremental cost-effectiveness ratio (ICER) were recorded. Results The unadjusted PartSA produced an ICER of £623,563, and after adjustment yielded an ICER range of £381,340–£386,907. The unadjusted MSM produced an ICER of £1,283,780. Adjusting OS in the MSM resulted in an ICER of £345,486. Conclusions The simplicity of the PartSA is lost when the decision problem becomes more complex (for example, when OS data are confounded by subsequent therapies). In this setting, the MSM structure may be considered more flexible, with fewer and less restrictive assumptions required versus the PartSA. Researchers should consider important study design features that may influence the generalisability of data when undertaking model conceptualisation.

Date: 2023
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DOI: 10.1007/s40258-023-00792-x

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