Modeling the economic outcomes of immuno-oncology drugs: Alternative model frameworks to capture clinical outcomes

E. J. Gibson, N. Begum, I. Koblbauer, G. Dranitsaris, D. Liew, P. McEwan, A. A.Tahami Monfared, Y. Yuan, A. Juarez-Garcia, D. Tyas, M. Lees

Research output: Contribution to journalArticleResearchpeer-review

5 Citations (Scopus)

Abstract

Background: Economic models in oncology are commonly based on the three-state partitioned survival model (PSM) distinguishing between progression-free and progressive states. However, the heterogeneity of responses observed in immuno-oncology (I-O) suggests that new approaches may be appropriate to reflect disease dynamics meaningfully. Materials and methods: This study explored the impact of incorporating immune-specific health states into economic models of I-O therapy. Two variants of the PSM and a Markov model were populated with data from one clinical trial in metastatic melanoma patients. Short-term modeled outcomes were benchmarked to the clinical trial data and a lifetime model horizon provided estimates of life years and quality adjusted life years (QALYs). Results: The PSM-based models produced short-term outcomes closely matching the trial outcomes. Adding health states generated increased QALYs while providing a more granular representation of outcomes for decision making. The Markov model gave the greatest level of detail on outcomes but gave short-term results which diverged from those of the trial (overstat-ing year 1 progression-free survival by around 60%). Conclusion: Increased sophistication in the representation of disease dynamics in economic models is desirable when attempting to model treatment response in I-O. However, the assumptions underlying different model structures and the availability of data for health state mapping may be important limiting factors.

Original languageEnglish
Pages (from-to)139-154
Number of pages16
JournalClinicoEconomics and Outcomes Research
Volume10
DOIs
Publication statusPublished - 8 Mar 2018

Keywords

  • Dacarbazine
  • Immuno therapy
  • Markov
  • Metastatic melanoma
  • Nivolumab
  • Partitioned survival

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