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

Abstract

BackgroundEconomic 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 methodsThis 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).ResultsThe 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%).ConclusionIncreased 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.

Cohort versus patient level simulation for the economic evaluation of single versus combination immuno-oncology therapies in metastatic melanoma

“…This paper extends a program of research being undertaken on innovative approaches to economic modeling and more accurate survival extrapolation 14 in I-O to represent clinical trial and long-term observational data in a more meaningful way. It builds on a previous comparison 15 of the 3-state PSM and extensions thereof vs a Markov modeling approach (a commonly used structure in health care evaluations) to represent I-O therapies ( Figure 2).…”

“…The "Methods: empirical analysis" section focuses on the methods used to estimate the PLS. As the methods underpinning the PSM-based and Markov models have been described elsewhere 15 , they are not reproduced in detail here. Considering that the choice of model is as much an empirical as a theoretical question 20 , the "Results" section compares the PLS results with those of the PSM-based and Markov models.…”

“…The PSM has become a standard approach in the evaluation of treatments for advanced and metastatic cancers 2 . Figures 2(a-c) illustrate the basic PSM structure which has been applied to the analysis of I-O therapies 21,22 , and the extensions of that model developed in previous research 15 . These structures are underpinned by K-M curves from the CheckMate 067 trial on OS (Figure 1(a)) and PFS (Figure 1(b)).…”

“…Exploratory analysis has illustrated that it is possible to augment the basic model with additional health states to capture the I-O response more appropriately, by defining health states in terms of treatment response 15 . Here, further assumptions are required to extend the analysis beyond the time horizon of the trial in the absence of well-established approaches to the extrapolation of response measures.…”

“…Full details of the methods used in developing the PSM and Markov approaches (lifetime models with monthly cycles) in I-O have been reported elsewhere 15 (with an overview provided in Table 1 and outcomes provided in Table 2).…”