Cohort versus patient level simulation for the economic evaluation of single versus combination immuno-oncology therapies in metastatic melanoma. (3rd June 2019)

• Record Type: Journal Article
• Authors: Gibson, Edward J.; Begum, Najida; Koblbauer, Ian; Dranitsaris, George; Liew, Danny; McEwan, Phil; Yuan, Yong; Juarez-Garcia, Ariadna; Tyas, David; Pritchard, Clive
• Is Part Of: Journal of medical economics. Volume 22:Number 6(2019)
• Page Start: 531
• Page End: 544
• Publication Date: 2019-06-03
• Subjects: Melanoma -- patient level simulation -- partitioned model -- Markov model -- immunotherapy -- modeling -- tumor growth -- progression free survival -- overall survival -- CheckMate 067; C51 -- C52; Medical care -- Cost control -- Periodicals; Medical economics -- Periodicals; 362.10941
• DOI: 10.1080/13696998.2019.1569446
• Languages: English
• ISSNs: 1369-6998

Abstract

Abstract: Background: Model structure, despite being a key source of uncertainty in economic evaluations, is often not treated as a priority for model development. In oncology, partitioned survival models (PSMs) and Markov models, both types of cohort model, are commonly used, but patient responses to newer immuno-oncology (I-O) agents suggest that more innovative model frameworks should be explored. Objective: A discussion of the theoretical pros and cons of cohort level vs patient level simulation (PLS) models provides the background for an illustrative comparison of I-O therapies, namely nivolumab/ipilimumab combination and ipilimumab alone using patient level data from the CheckMate 067 trial in metastatic melanoma. PSM, Markov, and PLS models were compared on the basis of coherence with short-term clinical trial endpoints and long-term cost per QALY outcomes reported. Methods: The PSM was based on Kaplan-Meier curves from CheckMate 067 with 3-year data on progression free survival (PFS) and overall survival (OS). The Markov model used time independent transition probabilities based on the average trajectory of PFS and OS over the trial period. The PLS model was developed based on baseline characteristics hypothesized to be associated with disease as well as significant mortality and disease progression risk factors identified through a proportional hazards model. Results: The short-term Markov model outputs matched the 1–3 year clinical trial results approximately as well as the PSMs for OS but not PFS. The fixed (average) cohort PLS results corresponded as well as the PSMs for OS in the combination therapy arm and PFS in the monotherapy arm. Over the lifetime horizon, the PLS produced an additional 5.95 quality adjusted life years (QALYs) associated with combination therapy relative to ipilimumab alone, resulting in an incremental cost-effectiveness ratio (ICER) of £6, 474 per QALY, compared with £14, 194 for the PSMs which gave an incremental benefit of between 2.2 and 2.4 QALYs. The Markov model was an outlier (∼ £49, 000 per QALY in the base case). Conclusions: The 4- and 5-state versions of the PSM cohort model estimated in this study deviate from the standard 3-state approach to better capture I-O response patterns. Markov and PLS approaches, by modeling state transitions explicitly, could be more informative in understanding I-O immune response, the PLS particularly so by reflecting heterogeneity in treatment response. However, both require a number of assumptions to capture the immune response effectively. Better I-O representation with surrogate endpoints in future clinical trials could yield greater model validity across all models.