AimsApproximately, 4% of Stage IV colorectal cancers (CRC) are microsatellite instability-high (MSI-H)/deficient mismatch repair (dMMR) tumors. Patients with metastatic MSI-H/dMMR CRC receiving conventional therapies experience lower response rates and tend to have worse overall survival compared with patients with microsatellite stable (MSS)/proficient mismatch repair (pMMR) CRC. Pembrolizumab received FDA approval in 2020 for first-line treatment of Stage IV MSI-H/dMMR CRC based on significantly longer progression-free survival versus standard of care (SoC, 5-fluorouracil-based therapy with or without bevacizumab or cetuximab). This study evaluated the cost-effectiveness of pembrolizumab vs. SoC as per KEYNOTE-177 and other first-line treatments for MSI-H/dMMR CRC from a US healthcare system perspective.MethodsA three-health-state partitioned-survival model was built using progression-free and overall survival data from KEYNOTE-177 and a network meta-analysis. Utilities were derived from KEYNOTE-177 EQ-5D-3L data. Drug acquisition, administration, AE, surgery, monitoring, subsequent treatment, and terminal care costs were included. Sensitivity and scenario analyses were performed, including utilizing a state-transition model structure and adopting a societal perspective.ResultsOver a lifetime time horizon, pembrolizumab and SoC were associated with total QALYs of 4.85 and 3.23, and total costs of $381,735 and $370,465, respectively, resulting in an ICER of $6,984 per QALY. QALY gains were mainly driven by extended survival with pembrolizumab. Pembrolizumab incurred higher drug acquisition costs relative to SoC but was cost-saving in terms of drug administration, AE, monitoring, subsequent treatment, and terminal care. Pembrolizumab dominated FOLFOX + panitumumab, FOLFOXIRI, and FOLFOXIRI + bevacizumab, and presented ICERs of $35,220 and $276 against XELOX and XELOX + bevacizumab. Results were robust to sensitivity and scenario analyses.ConclusionPembrolizumab is highly cost-effective for the first-line treatment of unresectable or metastatic MSI-H/dMMR CRC in the US at a willingness-to-pay threshold of $100,000/QALY.Key messagesPembrolizumab is a highly cost-effective option for the first-line treatment of patients with unresectable or metastatic MSI-H/dMMR colorectal cancer in the United States at a willingness-to-pay threshold of $100,000. Compared with the current standard of care for these patients, pembrolizumab:Increases survival due to delaying and preventing progression;Increases QALYs due to longer survival, improvement in HRQoL in the progression-free health state, and fewer Grade 3+ adverse events;Reduces costs associated with administering treatment, managing adverse events, monitoring post-progression disease, providing subsequent treatment, and providing terminal care; andReduces indirect health care costs when taking a societal perspective due to productivity gains from delaying and preventing progression and death, less frequent treatment administration and less frequent Grade 3+ adverse events.

Background: the National Institute for Health and Care Excellence (NICE) is responsible for ensuring that patients in England and Wales can access clinically and cost-effective treatments. However, NICE’s processes pose significant reimbursement challenges for treatments for rare diseases. While some orphan medicines have been appraised via the highly specialised technology route, most are appraised via the single technology appraisal programme, a route that is expected to be increasingly used given new more restrictive highly specialised technology criteria. This often results in delays to access owing to differences in applicable thresholds and the single technology appraisal approach being ill-equipped to deal with the inevitable decision uncertainty. NICE recently published their updated methods and process manual, which includes a new severity-of-disease modifier and an instruction to be more flexible when considering uncertainty in rare diseases. However, as the threshold gap between the single technology appraisal and highly specialised technology programmes remains, it is unlikely that these changes alone will address the problem. Objective: We explored the potential impact of quality-adjusted life-year weights in decision making. Methods: We explored the impact of NICE’s new severity-of-disease modifier weighting and two alternative methods (the use of alternative quality-adjusted life-year weights and the fair rate of return), using three recent single technology appraisals of orphan medicines (caplacizumab, teduglutide and pirfenidone for mild idiopathic pulmonary fibrosis). Results: Our results suggest NICE’s severity-of-disease modifier would not have affected the recommendations. Using alternative methods, based upon achievement of an incremental cost-effectiveness ratio below standard thresholds, patients could have received access to caplacizumab approximately 5 months earlier, and the appraisals for teduglutide and pirfenidone would have resulted in a positive recommendation following appraisal consultation meeting 1 when neither of these products was available over 5 years from the initial submission. Conclusion: Ultimately, moving from a restrictive end-of-life modifier to one based on disease severity is a more equitable approach likely to benefit many therapies, including orphan products. However, NICE’s single technology appraisal updates are unlikely to result in faster reimbursement of orphan medicines, nor will they address concerns around market access for orphan medicines in the UK.

IntroductionMixture modelling is increasingly being considered where a potential cure leads to a long life. Traditional methods use relative survival models for frail populations or cure models that have improper survival functions with theoretical infinite lifespans. Additionally, much of the work uses population data with long follow-up or theoretical data for method development.ObjectiveThis case study uses life table data to create a proper survival function in a real-world clinical trial context. In particular, we discuss the impact of the length of trial follow-up on the accuracy of model estimation and the impact of extrapolation to capture long-term survival.MethodsA review of recent National Institute for Health and Clinical Excellence (NICE) immuno-oncological and chimeric antigen receptor (CAR) T-cell therapy submissions was performed to assess industry uptake and NICE acceptance of survival analysis methods incorporating the potential for long-term survivorship. The case study analysed a simulated trial-based dataset investigating a curative treatment with long-term mortality based on population life tables. The analysis examined three timepoints corresponding to early trial, end-of-trial follow-up and complete follow-up. Mixture modelling approaches were considered, including both cure modelling and relative survival approaches. The curves were evaluated based on the ability to estimate cure fractions and mean life in years within the time span the models are based on and when extrapolating to capture long-term behaviour. The survival curves were fitted with Weibull distributions using non-mixture and mixture cure models.ResultsThe performance of the cure modelling methods depended on the relative maturity of the data, indicating that care is needed when deciding when the methods should be applied. For progression-free survival, the cure fraction simulated was 15%. The cure fractions estimated using the traditional mixture cure model were 43% (95% confidence interval [CI] 30-57) at the first analysis time point (40 months), 15% (95% CI 12-20) at the end-of-study follow-up (153 months) and 0% (95% CI 0-100) at the end of follow-up. Other standard cure modelling methods produced similar results. For overall survival, we observed a similar pattern of goodness of fit, with a good fit for the end-of-study follow-up and poor fit for the other two data cuts. However, in this case, the estimate of the cure fraction was below the true value in the first analysis data.ConclusionsThis case study suggests cure modelling works well with data in which the disease-specific events have had time to occur. Care is needed when extrapolating from immature data, and further information should support the estimation rather than relying on statistical estimates based on the trial alone.

BackgroundValidation of overall survival (OS) extrapolations of immune-checkpoint inhibitors (ICIs) during the National Institute for Health and Care Excellence (NICE) Single Technology Assessment (STA) process is limited due to data still maturing at the time of submission. Inaccurate extrapolation may lead to inappropriate decision-making. The availability of more mature trial data facilitates a retrospective analysis of the plausibility and validity of initial extrapolations. This study compares these extrapolations to subsequently available longer-term data.MethodsA systematic search of completed NICE appraisals of ICIs from March 2000 to December 2017 was performed. A targeted search was also undertaken to procure published OS data from the pivotal clinical trials for each identified STA made available post-submission to NICE. Initial Kaplan-Meier curves and associated extrapolations from NICE documentation were extracted to compare the accuracy of OS projections versus the most mature data.ResultsThe review identified 11 STAs, of which 10 provided OS data upon submission to NICE. The extrapolations undertaken considered parametric or piecewise survival models. Additional data cut-offs provided a mean of 18 months of OS beyond the end of the original data. Initial extrapolations typically under-estimated OS from the most mature data cut-off by 0.4-2.7%, depending on the choice of assessment method and use of the manufacturer- or ERG-preferred extrapolation.ConclusionLong-term extrapolation of OS is required for NICE STAs based on initial immature OS data. The results of this study demonstrate that the initial OS extrapolations employed by manufacturers and ERGs generally predicted OS reasonably well when compared to more mature data (when available), although on average they appeared to underestimate OS. This review and validation shows that, while the choice of OS extrapolation is uncertain, the methods adopted are generally aligned with later-published follow-up data and appear appropriate for informing HTA decisions.