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

Abstract

EJ Gibson,1 N Begum,1 I Koblbauer,1 G Dranitsaris,2 D Liew,3 P McEwan,4 AA Tahami Monfared,5,6 Y Yuan,7 A Juarez-Garcia,7 D Tyas,8 M Lees9 1Wickenstones Ltd, Didcot, UK; 2Augmentium Pharma Consulting Inc, Toronto, ON, Canada; 3Department of Epidemiology and Preventive Medicine, Alfred Hospital, Monash University, Melbourne, VIC, Australia; 4Health Economics and Outcomes Research Ltd, Cardiff, UK; 5Bristol-Myers Squibb Canada, Saint-Laurent, QC Canada; 6Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada; 7Bristol-Myers Squibb, Princeton, NJ, USA; 8Bristol-Myers Squibb, Uxbridge, UK; 9Bristol-Myers Squibb, Rueil-Malmaison, France 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 (overstating 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. Keywords: immuno therapy, metastatic melanoma, nivolumab, dacarbazine, Markov, partitioned survival  

Keywords

• Immuno-oncology
• metastatic melanoma
• nivolumab
• dacarbazine
• Markov
• partitioned survival