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

Background

Existing economic model frameworks may not adequately capture the atypical treatment response patterns in immuno-oncology (I-O) compared with conventional therapies and thus may fail to represent the full clinical value associated with disease dynamics and improved survival.

Objective

A cost-effectiveness analysis (CEA) of the I-O Regimen (nivolumab/ipilimumab) versus ipilimumab alone in advanced melanoma was carried out by applying a 5-state partitioned survival model (PSM) as a case study, to explore the I-O treatment response and clinical outcomes. The findings were compared with those of a conventional 3-state PSM.

Materials and Methods

The case study extends the conventional 3-state PSM, by separating the pre-progression state into non-responders and responders, and the post-progression state into normal and I-O progression to account for delayed treatment effects preceding clinical response. Model states were populated using patient-level data (where possible), mapping from the best overall response (BOR), and survival analysis with flexible and traditional parametric methods. Survival functions were applied to progression-free survival (PFS) and overall survival (OS) endpoints across treatment arms using the 4-year follow-up data (data available at the time of the research; since then 5-year follow-up data have been published) from the CheckMate 067 trial. Information on BOR was used as a means of differentiating the I-O treatment response in addition to the outcomes of progression-free and progressed disease. A UK National Health Service and personal social services (NHS/PSS) perspective over a lifetime horizon was used with outcomes discounted at 3.5% annually.

Results

The 5-state PSM generated an increase in quality adjusted life years (QALYs) in both treatment arms and gave a more granular description of patients’ health profiles compared with the traditional 3-state PSM. The incremental QALY increased by 13% (from 2.62 to 2.95 QALYs) and the incremental cost decreased by 12% (£29,125 to £25,678) with the 5-state model. In both models, the Regimen had an incremental cost-effectiveness ratio (ICER) relative to ipilimumab alone within the lower bound of the National Institute for Health and Care Excellence (NICE) reference range (£20,000 per QALY gained).

Conclusion

A 5-state economic model, incorporating relevant I-O health states, can be more informative to gain insight into treatment response and progression differences that are not commonly captured in existing economic models. Clinical trial endpoints, including those relating to treatment response, which are not directly reported in ongoing I-O trials, can be mapped on to the proposed modelled health states (although assumptions are required to do so). Improvements in reporting treatment response in future I-O clinical trials could help to further validate and improve the proposed model framework.

Healthcare cost comparison analysis of nivolumab in combination with ipilimumab versus nivolumab monotherapy and ipilimumab monotherapy in advanced melanoma

Background

Monoclonal antibodies targeting the cytotoxic T-lymphocyte-associated antigen 4 (CTLA4) (e.g., ipilimumab [IPI]) and the programmed cell death-1 (PD1) receptor (e.g., nivolumab [NIVO]) represent significant breakthroughs in the treatment of advanced melanoma. A combination of the 2 agents has demonstrated efficacy and survival benefits over NIVO or IPI monotherapy in treating advanced melanoma. We compared melanoma-specific costs following treatment with NIVO + IPI, NIVO monotherapy, or IPI monotherapy from the UK and German perspectives to ascertain whether these clinical benefits resulted in a cost advantage.

Methods

Patient-level resource utilization data for the three treatment cohorts were obtained from the CheckMate 067 trial (NCT01844505). All melanoma-specific resources, including drugs (index, concomitant and subsequent melanoma medications), office visits, emergency room visits, hospitalizations, lab tests, procedures and surgeries, utilized over a 48-month evaluation period after start of index treatment were included. Unit costs specific to each geography were applied from external sources. Mean costs per surviving patients were calculated for each successive 30-day period from treatment start and aggregated over the evaluation period.

Results

The total per-patient costs incurred by advanced melanoma patients over the 48-month period following treatment initiation with NIVO + IPI were 9% lower than NIVO monotherapy (£226k vs £248k) and 3% lower compared to IPI monotherapy (£226k vs. £233k) in the UK. In Germany, the total costs incurred by NIVO + IPI cohort were 5% lower than NIVO monotherapy (€258k vs €271k) and 4% lower compared to IPI monotherapy (€258k vs. €268k). Drug costs accounted for > 85% of total costs. Non-drug costs were slightly higher for NIVO + IPI and IPI monotherapy because of higher hospitalization rates. Costs incurred on subsequent drugs post progression were about 45% and 65% lower in NIVO + IPI cohort compared with NIVO and IPI monotherapy cohorts respectively.

Conclusions

The total costs incurred by a patient over a 48-month period following treatment initiation with NIVO + IPI are lower when compared with patients initiating monotherapies; further, the cost advantage is seen to be increasing over time. The clinical benefits offered by the regimen are thus supplemented by a cost advantage, as patients receiving either monotherapy treatment experience faster progression and, consequently, higher subsequent treatment costs. (Note: The cost results reported here are specific to the UK, and Germany, and may not be generalizable to other geographies).

Electronic supplementary material

The online version of this article (10.1186/s40164-019-0138-9) contains supplementary material, which is available to authorized users.

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

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.