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

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. ARTICLE HISTORY

<p>Economic Evaluation of Single versus Combination Immuno-Oncology Therapies: Application of a Novel Modelling Approach in Metastatic Melanoma</p>

“…Clinical endpoints of PFS, OS and BOR and common economic outcomes of LYs, QALYs and costs were considered in the evaluation. 10 The modelled outcomes for OS and PFS with the Regimen were more closely comparable with the reported trial endpoints than those in the ipilimumab arm over 4 years. This is driven by the best fits from the survival analysis.…”

“…5 As part of an ongoing investigation into innovative economic modelling approaches, this paper complements previous studies on the extrapolation of clinical benefit beyond the clinical trial period 7 and a comparison of cohort models 8 with a patient level simulation (PLS). 10 Throughout the research, the case study of malignant melanoma has been used as it is the condition in which the longest follow-up trial data in I-O is available (with ipilimumab). 11 The current study builds particularly on Gibson et al (2018) who compared a 3-state PSM and extensions thereof with a Markov model and found that the PSM-based approaches generated results more closely comparable with existing trial data endpoints than the Markov model, 8 as expected given the way in which clinical data was reported.…”

Challenges in synthesising cost-effectiveness estimates

“…decision trees, state-transition models, patient simulations) which should theoretically yield the same conclusion about the cost effectiveness of an intervention, however, due to the diverging use of data and assumptions made results are likely to differ. For example, in oncology, published examples have demonstrated that different model structures estimate different durations spent in key health states, impacting cost-effectiveness results [8][9][10][11][12]. Despite this, in the same disease area, justification for the choice of model structure is often minimal [13].…”

Advances in health economic models and outcomes: a necessary condition to make advances in healthcare policy

“…Again, advances are made with the trend towards more patient level modeling as compared to cohort-based models. Although in several situations and conditions the patient level approach might not contribute much to the final result and assessment as compared to cohort based models, in the case of immune-oncology, Gibson et al 4 show that the patient level approach does much better reflect heterogeneity in treatment response, leading to a more valid and reliable estimate of future QALYs.…”