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

ClinicoEconomics and Outcomes Research Modeling the economic outcomes of immuno- oncology drugs: alternative model frameworks to capture clinical outcomes N Begum 2 I Koblbauer 2 G Dranitsaris 1 D Liew 0 P McEwan 6 AA Tahami Monfared 4 5 Y Yuan 3 A Juarez-Garcia 3 D Tyas 8 M Lees 7 0 Department of Epidemiology and Preventive Medicine, Alfred Hospital, Monash University , Melbourne, VIC , Australia 1 Augmentium Pharma Consulting Inc , Toronto, ON , Canada 2 Wickenstones Ltd , Didcot , UK 3 Bristol-Myers Squibb , Princeton, NJ , USA 4 Department of Epidemiology , Biostatistics, and Occupational Health , McGill University , Montreal, QC , Canada 5 Bristol- Myers Squibb Canada , Saint- Laurent, QC Canada 6 Health Economics and Outcomes Research Ltd , Cardiff , UK 7 Bristol-Myers Squibb , Rueil- Malmaison , France 8 Bristol-Myers Squibb , Uxbridge , UK 8 1 0 2 - l u J - 2 1 n o 7 0 2 . 6 4 . 9 5 . 7 3 y b / m o c . s s e r p e v o d . ww l.y /w no / : tsp sue th la ldedao rpeoF PowerdbyTCPDF(ww.tcpdf.org) 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. immuno therapy; metastatic melanoma; nivolumab; dacarbazine; Markov; parti- - n w o d h c r a e s e R s e m o c t u O d n a s c i m o n o c E o c i n il C open access to scientific and medical research Introduction Reliable evidence to determine long-term cost-effectiveness is frequently absent when a marketing authorization application is submitted for a new therapy. This evidence is central to recent debates around the affordability of cancer drugs and the sustainability of high drug prices.1–3 The tension between effective management of health care budgets and the provision of optimal care to cancer patients highlights the need for decision makers to assess the true value of cancer treatments using the most rigorous and robust methods.4,5 Together with the challenge of affordability, an accurate depiction of the true impact of anticancer medication is critical particularly given escalating price benchmarks accompanied by, in some cases, only moderate improvements in overall survival (OS). Comparing treatment effect can be complex; this is particularly true given the numerous surrogate endpoints often used in clinical trials which may, or may not, be predictive of a true OS benefit.1,6 In the evaluation of new treatments, a balance between disease progression and treatment-related toxicities is key to determining clinically meaningful outcomes 2801 fcoormppaatriiesnotnss.,TtoheiEmuprroopveeancrSoosscipetryodfourctMaenddicdailsOeansceolaorgeya l--Ju (ESMO) has developed a reproducible tool which can be 12n applied to new anticancer treatments to assess the mag7o nitude of clinical benefit (ESMO Magnitude of Clinical .620 Benefit Scale). The tool grades each trial within the curative ..954 and non-curative disease settings using a two-part rule. y37 Firstly, the lower limit of the 95% confidence interval (CI) /b on the hazard ratio is compared with specified thresholds. .com Secondly, the observed absolute difference in treatment rsse outcomes is compared with the minimum absolute gain vpeo considered as beneficial.7,8 .dww l.y As more innovative treatments are introduced, the value /w no defined by conventional economic modeling requires renewed / : tsp sue scrutiny given the complex technical and data availability thom lsaon issues raised by new therapies.4,5 fr r An established approach to economic modeling in oncolldedao rpeoF ogy is the three-state partitioned survival model (PSM), nw which classifies patients into the s (...truncated)