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Objective The aim of this study was to investigate the cost effectiveness of nivolumab versus docetaxel in previously treated, advanced non–small-cell lung cancer (NSCLC) in England and assess how conditional reimbursement within the Cancer Drugs Fund (CDF) can be used to ensure timely patient access to effective treatments. Methods Cost-effectiveness models developed for the National Institute for Health and Care Excellence (NICE) TA483 (squamous) and TA484 (non-squamous) technology appraisals were supplemented with updated overall survival (OS), progression-free survival (PFS), and time-to-treatment discontinuation data collected as part of the CDF data collection agreement. Both models were developed by using a partitioned-survival approach based on PFS and OS predictions from CheckMate 017 and CheckMate 057 to estimate the projected proportion of patients in each health state (progression free, progression, death) throughout the model’s time horizon. The primary outcomes were estimated costs, quality-adjusted life-years (QALYs), and the resulting incremental cost-effectiveness ratio (ICER) expressed as cost/QALY gained. Results Base-case ICERs for treating patients with nivolumab versus docetaxel were £35,657/QALY and £38,703/QALY for squamous and non-squamous NSCLC patients, respectively, which are substantially lower than those obtained from what were deemed to be the most appropriate analyses for decision making in the original submissions when run with the same patient access scheme discount: £68,576/QALY and £73,189/QALY gained for squamous and non-squamous NSCLC, respectively. Conclusions Nivolumab versus docetaxel is cost effective for treating locally advanced/metastatic NSCLC after prior chemotherapy in adults, regardless of tumour histology or programmed death-ligand 1 expression status. Supplementary Information The online version contains supplementary material available at 10.1007/s41669-020-00245-4.

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Purpose: Cost-effectiveness analyses (CEA) of new technologies typically include “background” costs (eg, all “related” health care costs other than the specific technology under evaluation) as well as drug costs. In oncology, these are often expensive. The marginal cost-effectiveness ratio (ie, the extra costs and QALYs associated with each extra period of survival) calculates the ratio of background costs to QALYs during post-progression. With high background costs, the incremental cost-effectiveness ratio (ICER) can become less favorable as survival increases and the ICER moves closer to the marginal cost-effectiveness ratio, making cost-effectiveness prohibitive. This study assessed different methods to determine whether high ICERs are caused by high drug costs, high “background costs” or a combination of both and how different approaches can alter the impact of background costs on the ICER where the marginal cost-effectiveness ratio is close to, or above, the cost-effectiveness threshold. Methods: The National Institute for Health and Care Excellence oncology technology appraisals published or updated between October 2012 and October 2017 were reviewed. A case study was selected, and the CEA was replicated. Three modeling approaches were tested on the case study model. Results: Applying one-off “transition” costs during post-progression reduced the ongoing “incremental” costs of survival, which meant that the marginal cost-effectiveness ratio was substantially reduced and problems associated with additional survival were less likely to impact the ICER. Similarly, the use of two methods of additional utility weighting for end-of-life cases meant that the marginal cost-effectiveness ratio was reduced proportionally, again lessening the impact of increased survival. Conclusion: High ICERs can be caused by factors other than the cost of the drug being assessed. The economic models should be correct and valid, reflecting the true nature of marginal survival. Further research is needed to assess how alternative approaches to the measurement and application of background costs and benefits may provide an accurate assessment of the incremental benefits of life-extending oncology drugs. If marginal survival costs are incorrectly calculated (ie, by summing total post-progressed costs and dividing by the number of baseline months in that state), then the costs of marginal survival are likely to be overstated in economic models.

matrices with four, seven, ten and twenty health states. Results: In theory, there is no generally applicable correct transformation method. Based on our simulations, SP resulted in the smallest transformation induced discrepancies for generated annual transition matrices for two treatment strategies (relative difference between matrices SP:4.66*10 216 ; C:0.00835). E showed slightly smaller discrepancies than SP when one of the direct transitions between health states was excluded. For longterm outcomes, the largest discrepancy occurred for estimated costs applying method C (up to 10%). For higher dimensional models, E performs best. Conclusions: In our breast-cancer example, matrix transformations (E, SP) perform better than transforming all transition probabilities separately (C). Transition probabilities based on alternative conversion methods should, therefore, be applied in sensitivity analyses to see the impact on model outcomes (ICER and proposed decision). There is a need to add alternative transformation methods to decision-analytic software tools.