Cost-effectiveness analysis for avelumab first-line maintenance treatment of advanced urothelial carcinoma in Scotland.

Aim: The cost-effectiveness of avelumab first-line maintenance treatment for locally advanced or metastatic urothelial carcinoma in Scotland was assessed. Materials & methods: A partitioned survival model was developed comparing avelumab plus best supportive care (BSC) versus BSC alone, incorporating JAVELIN Bladder 100 trial data, costs from national databases and published literature and clinical expert validation of assumptions. Incremental cost-effectiveness ratio (ICER) was estimated using lifetime costs and quality-adjusted life-years (QALY). Results: Avelumab plus BSC had incremental costs of £9446 and a QALY gain of 0.63, leading to a base-case (deterministic) ICER of £15,046 per QALY gained, supported by robust sensitivity analyses. Conclusion: Avelumab first-line maintenance is likely to be a cost-effective treatment for locally advanced or metastatic urothelial carcinoma in Scotland.

What is this article about? This study looked at the costs of avelumab when given as maintenance treatment for people in Scotland with advanced urothelial carcinoma, compared with the longer survival and other benefits that it provides. How was this done? Researchers estimated the costs and treatment benefits expected with avelumab using data from a clinical trial called JAVELIN Bladder 100, national databases, data from previously published studies and expert opinions. What were the results? Costs associated with using avelumab maintenance treatment for people with advanced urothelial carcinoma in Scotland were considered to be acceptable based on the benefits it provides. What do the results of the study mean? These results support the use of avelumab first-line maintenance as a standard treatment for people with advanced urothelial carcinoma in Scotland.

A UK cost-effectiveness analysis of trifluridine/tipiracil for heavily pretreated metastatic gastroesophageal cancers.

Background: The current work was designed to estimate the cost-effectiveness of trifluridine/tipiracil (T/T) versus best supportive care (BSC) for patients with advanced stage or metastatic gastroesophageal cancer (mGC) from a UK perspective. Materials & methods: A partitioned survival analysis was undertaken using data from the phase III TAGS trial. A jointly fitted lognormal model was selected for overall survival and individual generalized gamma models were chosen for progression-free survival and time-to-treatment-discontinuation. The primary outcome was the cost per quality-adjusted life year (QALY) gained. Sensitivity analyses were undertaken to investigate uncertainty. Results: Compared with BSC, T/T was associated with a cost per QALY gained of £37,907. Conclusion: T/T provides a cost-effective treatment option for mGC in the UK setting.

Predicting Survival for Chimeric Antigen Receptor T-Cell Therapy: A Validation of Survival Models Using Follow-Up Data From ZUMA-1.

OBJECTIVES: Survival extrapolation for chimeric antigen receptor T-cell therapies is challenging, owing to their unique mechanistic properties that translate to complex hazard functions. Axicabtagene ciloleucel is indicated for the treatment of relapse or refractory diffuse large B-cell lymphoma after 2 or more lines of therapy based on the ZUMA-1 trial. Four data snapshots are available, with minimum follow-up of 12, 24, 36, and 48 months. This analysis explores how survival extrapolations for axicabtagene ciloleucel using ZUMA-1 data can be validated and compared. METHODS: Three different parametric modeling approaches were applied: standard parametric, spline-based, and cure-based models. Models were compared using a range of metrics, across the 4 data snapshot, including visual fit, plausibility of long-term estimates, statistical goodness of fit, inspection of hazard plots, point-estimate accuracy, and conditional survival estimates. RESULTS: Standard and spline-based parametric extrapolations were generally incapable of fitting the ZUMA-1 data well. Cure-based models provided the best fit based on the earliest data snapshot, with extrapolations remaining consistent as data matured. At 48 months, the maximum survival overestimate was 8.3% (Gompertz mixture-cure model) versus the maximum underestimate of 33.5% (Weibull standard parametric model). CONCLUSIONS: Where a plateau in the survival curve is clinically plausible, cure-based models may be helpful in making accurate predictions based on immature data. The ability to reliably extrapolate from maturing data may reduce delays in patient access to potentially lifesaving treatments. Additional research is required to understand how models compare in broader contexts, including different treatments and therapeutic areas.

"Stick or Twist?" Negotiating Price and Data in an Era of Conditional Approval.

BACKGROUND: Changes in the regulatory context enable faster approval of transformative medicines. They also lead to health technology assessment (HTA) agencies having to make decisions with less evidence. In response, HTA agencies have also initiated forms of conditional approval. When the evidence base for a new oncology treatment leaves substantial uncertainty, the new Cancer Drugs Fund allows the National Institute for Heath and Care Excellence to give the manufacturer two options: (1) offer a low price based on conservative assumptions and obtain immediate approval ("stick") or (2) wait until the evidence base has further matured before finalizing a potentially higher agreed price ("twist"). OBJECTIVES: The purpose of this article is to explain how, using the theoretical framework of the expected value of sample information, simulation methods can help inform a manufacturer's decisions when faced with the option to stick or twist. METHODS: We first summarize a general model to help frame the manufacturer's negotiating strategy. We then use a motivating case study, based on a hypothetical immunotherapy, to illustrate how manufacturers can use simulation methods to robustly characterize the uncertainty inherent to further data collection and incorporate this uncertainty within their decision making. RESULTS: Our approach allows us to estimate the commercial value of generating additional data (the difference between the estimated net present value of stick and twist). We test the sensitivity of the results to different assumptions via scenario analyses. CONCLUSIONS: This article shows that simulation methods can be used to help pharmaceutical managers make informed strategic decisions in contexts of uncertainty.

Evaluation of survival extrapolation in immuno-oncology using multiple pre-planned data cuts: learnings to aid in model selection.

BACKGROUND: Due to limited duration of follow up in clinical trials of cancer treatments, estimates of lifetime survival benefits are typically derived using statistical extrapolation methods. To justify the method used, a range of approaches have been proposed including statistical goodness-of-fit tests and comparing estimates against a previous data cut (i.e. interim data collected). In this study, we extend these approaches by presenting a range of extrapolations fitted to four pre-planned data cuts from the JAVELIN Merkel 200 (JM200) trial. By comparing different estimates of survival and goodness-of-fit as JM200 data mature, we undertook an iterative process of fitting and re-fitting survival models to retrospectively identify early indications of likely long-term survival. METHODS: Standard and spline-based parametric models were fitted to overall survival data from each JM200 data cut. Goodness-of-fit was determined using an assessment of the estimated hazard function, information theory-based methods and objective comparisons of estimation accuracy. Best-fitting extrapolations were compared to establish which one provided the most accurate estimation, and how statistical goodness-of-fit differed. RESULTS: Spline-based models provided the closest fit to the final JM200 data cut, though all extrapolation methods based on the earliest data cut underestimated the 'true' long-term survival (difference in restricted mean survival time [RMST] at 36 months: - 1.1 to - 0.5 months). Goodness-of-fit scores illustrated that an increasingly flexible model was favored as data matured. Given an early data cut, a more flexible model better aligned with clinical expectations could be reasonably justified using a range of metrics, including RMST and goodness-of-fit scores (which were typically within a 2-point range of the statistically 'best-fitting' model). CONCLUSIONS: Survival estimates from the spline-based models are more aligned with clinical expectation and provided a better fit to the JM200 data, despite not exhibiting the definitively 'best' statistical goodness-of-fit. Longer-term data are required to further validate extrapolations, though this study illustrates the importance of clinical plausibility when selecting the most appropriate model. In addition, hazard-based plots and goodness-of-fit tests from multiple data cuts present useful approaches to identify when a more flexible model may be advantageous. TRIAL REGISTRATION: JAVELIN Merkel 200 was registered with ClinicalTrials.gov as NCT02155647 on June 4, 2014.

Real-world evidence use in assessments of cancer drugs by NICE.

OBJECTIVE: To establish how real-world evidence (RWE) has been used to inform single technology appraisals (STAs) of cancer drugs conducted by the National Institute for Health and Care Excellence (NICE). METHODS: STAs published by NICE from April 2011 to October 2018 that evaluated cancer treatments were reviewed. Information regarding the use of RWE to directly inform the company-submitted cost-effectiveness analysis was extracted and categorized by topic. Summary statistics were used to describe emergent themes, and a narrative summary was provided for key case studies. RESULTS: Materials for a total of 113 relevant STAs were identified and analyzed, of which nearly all (96 percent) included some form of RWE within the company-submitted cost-effectiveness analysis. The most common categories of RWE use concerned the health-related quality of life of patients (71 percent), costs (46 percent), and medical resource utilization (40 percent). While sources of RWE were routinely criticized as part of the appraisal process, we identified only two cases where the use of RWE was overtly rejected; hence, in the majority of cases, RWE was accepted in cancer drug submissions to NICE. DISCUSSION: RWE has been used extensively in cancer submissions to NICE. Key criticisms of RWE in submissions to NICE are seldom regarding the use of RWE in general; instead, these are typically concerned with specific data sources and the applicability of these to the decision problem. Within an appropriate context, RWE constitutes an extremely valuable source of information to inform decision making; yet the development of best practice guidelines may improve current reporting standards.

Survival Extrapolation in Cancer Immunotherapy: A Validation-Based Case Study.

BACKGROUND: Immune-checkpoint inhibitors may provide long-term survival benefits via a cured proportion, yet data are usually insufficient to prove this upon submission to health technology assessment bodies. OBJECTIVE: We revisited the National Institute for Health and Care Excellence assessment of ipilimumab in melanoma (TA319). We used updated data from the pivotal trial to assess the accuracy of the extrapolation methods used and compared these to previously unused techniques to establish whether an alternative extrapolation may have provided more accurate survival projections. METHODS: We compared projections from the piecewise survival model used in TA319 and those produced by alternative models (fit to trial data with minimum follow-up of 3 years) to a longer-term data cut (5-year follow-up). We also compared projections to external data to help assess validity. Alternative approaches considered were parametric, spline-based, mixture, and mixture-cure models. RESULTS: Only the survival model used in TA319 and a mixture-cure model provided 5-year survival predictions close to those observed in the 5-year follow-up data set. Standard parametric, spline, and non-curative-mixture models substantially underestimated 5-year survival. Survival estimates from the TA319 model and the mixture-cure model diverge considerably after 5 years and remain unvalidated. CONCLUSIONS: In our case study, only models that incorporated an element of external information (through a cure fraction combined with background mortality rates or using registry data) provided accurate estimates of 5-year survival. Flexible models that were able to capture the complex hazard functions observed during the trial, but which did not incorporate external information, extrapolated poorly.

"Don't keep me waiting": estimating the impact of reduced vein-to-vein time on lifetime US 3L+ LBCL patient outcomes.

ABSTRACT: Chimeric antigen receptor T-cell therapy (CAR T) has revolutionized the treatment of hematological cancers. Its production requires a complex logistical process, and the time from leukapheresis to patient infusion (known as the vein-to-vein time [V2VT]) can be long during which a patients clinical condition may deteriorate. This study was designed to estimate the benefits of reduced V2VT for third-line or later (3L+) relapsed/refractory large B-cell lymphoma (R/R LBCL) patients treated with CAR T. A mathematical model was developed to estimate the lifetime outcomes of a hypothetical cohort of patients who had either a long or short V2VT. Life-years (LYs), quality-adjusted LYs (QALYs), and costs were estimated. Scenario analyses were performed to assess the robustness of results to key assumptions. The results of the model show that reducing V2VT from 54 days (tisa-cel median V2VT; JULIET) to 24 days (axi-cel median V2VT; ZUMA-1) led to a 3.2-year gain in life expectancy (4.2 vs 7.7 LYs), and 2.4 additional QALYs (3.2 vs 5.6) per patient. Furthermore, a shorter V2VT was shown to be cost-effective under conventional willingness-to-pay thresholds in the United States. Results are driven by a higher infusion rate and a better efficacy of CAR T for those infused. Scenario analyses using a smaller difference in V2VT (24 vs 36 days) produced consistent results. Our study is the first to quantify lifetime V2VT-related outcomes for 3L+ R/R LBCL patients treated with CAR T utilizing currently available evidence. Shorter V2VTs led to improved outcomes, demonstrating the importance of timely infusion achievable by faster manufacturing times and optimization of hospital delivery.

Advances in Addressing Patient Heterogeneity in Economic Evaluation: A Review of the Methods Literature.

Cost-effectiveness analyses commonly use population or sample averages, which can mask key differences across subgroups and may lead to suboptimal resource allocation. Despite there being several new methods developed over the last decade, there is no recent summary of what methods are available to researchers. This review sought to identify advances in methods for addressing patient heterogeneity in economic evaluations and to provide an overview of these methods. A literature search was conducted using the Econlit, Embase and MEDLINE databases to identify studies published after 2011 (date of a previous review on this topic). Eligible studies needed to have an explicit methodological focus, related to how patient heterogeneity can be accounted for within a full economic evaluation. Sixteen studies were included in the review. Methodologies were varied and included regression techniques, model design and value of information analysis. Recent publications have applied methodologies more commonly used in other fields, such as machine learning and causal forests. Commonly noted challenges associated with considering patient heterogeneity included data availability (e.g., sample size), statistical issues (e.g., risk of false positives) and practical factors (e.g., computation time). A range of methods are available to address patient heterogeneity in economic evaluation, with relevant methods differing according to research question, scope of the economic evaluation and data availability. Researchers need to be aware of the challenges associated with addressing patient heterogeneity (e.g., data availability) to ensure findings are meaningful and robust. Future research is needed to assess whether and how methods are being applied in practice.

An Investigation into the Relationship Between Choice of Model Structure and How to Adjust for Subsequent Therapies Using a Case Study in Oncology.

BACKGROUND: A common challenge in health technology assessments (HTAs) of cancer treatments is how subsequent therapy use within the trial follow-up may influence cost-effectiveness model outcomes. Although overall survival (OS) is often a key driver of model results, there are no guidelines to advise how to adjust for this potential confounding, with different approaches available dependent on the model structure. OBJECTIVE: We compared a partitioned survival analysis (PartSA) with a semi-Markov multi-state model (MSM) structure, with and without attempts to adjust for the impact of subsequent therapies on OS using a case study describing outcomes for people with relapsed/refractory multiple myeloma. METHODS: Both model structures included three health states: pre-progression, progressed disease and death. Three traditional crossover methods were considered within the context of the PartSA, whereas for the MSM, the probability of post-progression death was pooled across arms. Impacts on the model incremental cost-effectiveness ratio (ICER) were recorded. RESULTS: The unadjusted PartSA produced an ICER of £623,563, and after adjustment yielded an ICER range of £381,340-£386,907. The unadjusted MSM produced an ICER of £1,283,780. Adjusting OS in the MSM resulted in an ICER of £345,486. CONCLUSIONS: The simplicity of the PartSA is lost when the decision problem becomes more complex (for example, when OS data are confounded by subsequent therapies). In this setting, the MSM structure may be considered more flexible, with fewer and less restrictive assumptions required versus the PartSA. Researchers should consider important study design features that may influence the generalisability of data when undertaking model conceptualisation.

De Novo Cost-Effectiveness Model Framework for Nonalcoholic Steatohepatitis-Modeling Approach and Validation.

BACKGROUND: Nonalcoholic steatohepatitis (NASH) is a chronic liver disease associated with hepatic morbidity and mortality and extra-hepatic comorbidities. Published NASH cost-effectiveness models (CEMs) are heterogeneous and consistently omit comorbid conditions that frequently co-exist alongside NASH. We aimed to develop a de novo CEM framework that incorporates extra-hepatic disease states and outcomes alongside hepatic components to enable future estimation of the cost-effectiveness of NASH interventions. METHODS: Patient-level simulation and cohort-level Markov models were implemented in the same framework. Model inputs included fibrosis progression; late-stage liver disease outcomes; comorbidity outcomes for cardiovascular disease, type 2 diabetes, and obesity; mortality; health-related quality of life; and direct medical costs. The prototype analysis assessed the cost-effectiveness of obeticholic acid versus standard of care from a US payer perspective over a lifetime horizon with costs and effects discounted at 3% per annum. However, the CEM was designed for easy adaptation to other countries, time horizons, and other considerations. Efficacy and adverse event parameters were obtained from the 18-month interim analysis of the REGENERATE trial. Outputs include total and incremental costs, total life years, and quality-adjusted life years. RESULTS: In this model, total costs, total life years, and quality-adjusted life years were all higher with obeticholic acid compared with standard of care. Cross-validation of this model with the 2016 and 2020 Institute for Clinical and Economic Review models revealed marked differences, mainly driven by mortality inputs, transition probability estimates, and incorporation of the effect of treatment and comorbidities. CONCLUSION: This is the first CEM in NASH to incorporate the clinical consequences of several comorbidities. The flexible yet standardized framework permits estimation of the cost-effectiveness of NASH interventions in a variety of settings. The model currently includes several assumptions and will be further developed as more relevant data become available.

A Systematic Review of Methods to Incorporate External Evidence into Trial-Based Survival Extrapolations for Health Technology Assessment.

BACKGROUND: External evidence is commonly used to inform survival modeling for health technology assessment (HTA). While there are a range of methodological approaches that have been proposed, it is unclear which methods could be used and how they compare. PURPOSE: This review aims to identify, describe, and categorize established methods to incorporate external evidence into survival extrapolation for HTA. DATA SOURCES: Embase, MEDLINE, EconLit, and Web of Science databases were searched to identify published methodological studies, supplemented by hand searching and citation tracking. STUDY SELECTION: Eligible studies were required to present a novel extrapolation approach incorporating external evidence (i.e., data or information) within survival model estimation. DATA EXTRACTION: Studies were classified according to how the external evidence was integrated as a part of model fitting. Information was extracted concerning the model-fitting process, key requirements, assumptions, software, application contexts, and presentation of comparisons with, or validation against, other methods. DATA SYNTHESIS: Across 18 methods identified from 22 studies, themes included use of informative prior(s) (n = 5), piecewise (n = 7), and general population adjustment (n = 9), plus a variety of "other" (n = 8) approaches. Most methods were applied in cancer populations (n = 13). No studies compared or validated their method against another method that also incorporated external evidence. LIMITATIONS: As only studies with a specific methodological objective were included, methods proposed as part of another study type (e.g., an economic evaluation) were excluded from this review. CONCLUSIONS: Several methods were identified in this review, with common themes based on typical data sources and analytical approaches. Of note, no evidence was found comparing the identified methods to one another, and so an assessment of different methods would be a useful area for further research.HighlightsThis review aims to identify methods that have been used to incorporate external evidence into survival extrapolations, focusing on those that may be used to inform health technology assessment.We found a range of different approaches, including piecewise methods, Bayesian methods using informative priors, and general population adjustment methods, as well as a variety of "other" approaches.No studies attempted to compare the performance of alternative methods for incorporating external evidence with respect to the accuracy of survival predictions. Further research investigating this would be valuable.

Out of Date or Best Before? A Commentary on the Relevance of Economic Evaluations Over Time.

The impact of time on the applicability and relevance of historical economic evaluations can be considerable. Ignoring this may lead to the use of weak or invalid evidence to inform important research questions or resource allocation decisions, as historical economic evaluations may have reached different conclusions compared to if a similar study had been conducted more recently. There are multiple factors that contribute towards evidence becoming outdated including changes to the relevant decision problem (e.g. comparators), changes to parameters (such as costs, utilities and resource use) and methodological updates (e.g. recommendations on uncertainty analysis). Researchers reviewing economic evaluations need to consider whether changes over time would influence the study design and results if the evaluation were repeated, to the extent that it is no longer helpful or informative. In this paper, we summarise these key issues and make recommendations about how and whether researchers can future proof their economic evaluations.

What is the Impact of the Analysis Method Used for Health State Utility Values on QALYs in Oncology? A Simulation Study Comparing Progression-Based and Time-to-Death Approaches.

BACKGROUND: Health state utility values ('utilities') are an integral part of health technology assessment. Though traditionally categorised by disease status in oncology (i.e. progression), several recent assessments have adopted values calculated according to the time that measures were recorded before death. We conducted a simulation study to understand the limitations of each approach, with a focus on mismatches between the way utilities are generated, and analysed. METHODS: Survival times were simulated based on published literature, with permutations of three utility generation mechanisms (UGMs) and utility analysis methods (UAMs): (1) progression based, (2) time-to-death based, and (3) a 'combination approach'. For each analysis quality-adjusted life-years (QALYs) were estimated. Goodness of fit was assessed via percentage mean error (%ME) and mean absolute error (%MAE). Scenario analyses were performed varying individual parameters, with complex scenarios mimicking published studies. The statistical code is provided for transparency and to aid future work in the area. RESULTS: %ME and %MAE were lowest when the correct analysis form was specified (i.e. UGM and UAM aligned). Underestimates were produced when a time-to-death element was present in the UGM but not included in the UAM, while the 'combined' UAM produced overestimates irrespective of the UGM. Scenario analysis demonstrated the importance of the volume of available data beyond the initial time period, for example follow-up. CONCLUSIONS: We show that the use of an incorrectly or over-specified UAM can result in substantial bias in the estimation of utilities. We present a flowchart to highlight the issues that may be faced.

Cost-Effectiveness Analysis of rFVIIIFc Versus Contemporary rFVIII Treatments for Patients with Severe Hemophilia A Without Inhibitors in the United States.

BACKGROUND: A range of treatments for patients with severe hemophilia A (HA) have been developed over the last decade, allowing for reduced frequency of administration and improved outcomes (joint health and breakthrough bleeding rates). While clinically effective, the cost effectiveness of these treatments has not been established. OBJECTIVE: This study presents a cost-effectiveness analysis of contemporary rFVIII treatments for severe HA patients without inhibitors. METHODS: A published semi-Markov model was used to compare three different prophylaxis regimens: (1) extended half-life (EHL) recombinant Factor VIII (rFVIII) Fc-fusion protein (rFVIIIFc, Eloctate®, Sanofi), (2) EHL PEGylated rFVIII (PEG-rFVIII, Adynovate®, Takeda), and (3) standard half-life (SHL) rFVIII (antihemophilic factor [recombinant], Advate®, Takeda), used as a proxy for all SHL rFVIII treatments. Acquisition costs were included based on published dosing and weight data. Benefits were incorporated through published annualized bleeding rates, rates of target joint development/resolution, and improvements in the modified hemophilia joint health score. Results were presented as total, discounted costs, and quality-adjusted life-years (QALYs). RESULTS: rFVIIIFc was shown to provide the most QALYs (27.922) compared with both PEG-rFVIII (27.454) and SHL rFVIII (27.071), at lower costs. Discounted lifetime costs were estimated at US$18.235m (rFVIIIFc), US$20.198m (PEG-rFVIII), and US$18.285m (SHL rFVIII), and were predominantly affected by model settings related to acquisition costs, patient weight, and dosing. CONCLUSIONS: rFVIIIFc may offer a cost-effective option for severe HA patients. Uncertainties owing to the limited evidence base is the main limitation of the study.

An Economic Comparison of Treatment Strategies with Anakinra in Systemic Juvenile Idiopathic Arthritis (sJIA).

INTRODUCTION: Systemic juvenile idiopathic arthritis (sJIA) is a rare, complex autoinflammatory disease with substantial morbidity, often characterized by fever, rash, and muscle pain, amongst other symptoms. Biologic agents, such as anakinra, have been successfully used to treat patients internationally, but their usage in some regions is limited to patients that have failed to achieve clinically inactive disease with corticosteroids and conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs). Use of anakinra early in the disease course leads to better clinical outcomes; however, longer-term costs for this treatment strategy have not been established. This study compares the economic implications of first-line versus later-line availability of anakinra for patients with sJIA. METHODS: Data for patients treated with first-line anakinra were identified from a single-center, prospective study and compared to a combination of published trial and economic evaluation information to facilitate a comparison to later-line anakinra (ie, following corticosteroids + csDMARDs). Costs were estimated for product acquisition and medical resource utilization (MRU), including planned outpatient visits and unplanned hospital admissions. Total costs over a 5-year horizon were compared. RESULTS: Total 5-year product acquisition cost for the first-line anakinra strategy was €24,021, and for later-line anakinra was €20,471. The corresponding MRU costs were €19,197 (first-line) versus €25,425 (later-line). Overall 5-year costs (product acquisition and MRU) were lower for the first-line strategy (€43,218 versus €45,896). CONCLUSION: The use of anakinra for patients with sJIA in the first-line setting is efficacious to induce and sustain inactive disease, and the findings of this study show that this treatment strategy leads to cost savings through reduced medical expenditure.

A comparison of partitioned survival analysis and state transition multi-state modelling approaches using a case study in oncology.

AIMS: To construct and compare a partitioned-survival analysis (PartSA) and a semi-Markov multi-state model (MSM) to investigate differences in estimated cost effectiveness of a novel cancer treatment from a UK perspective. MATERIALS AND METHODS: Data from a cohort of late-stage cancer patients (N > 700) enrolled within a randomized, controlled trial were used to populate both modelling approaches. The statistical software R was used to fit parametric survival models to overall survival (OS) and progression-free survival (PFS) data to inform the PartSA (package "flexsurv"). The package "mstate" was used to estimate the MSM transitions (permitted transitions: (T1) "progression-free" to "dead", (T2) "post-progression" to "death", and (T3) "pre-progression" to "post-progression"). Key costs included were treatment-related (initial, subsequent, and concomitant), adverse events, hospitalizations and monitoring. Utilities were stratified by progression. Outcomes were discounted at 3.5% per annum over a 15-year time horizon. RESULTS: The PartSA and MSM approaches estimated incremental cost-effectiveness ratios (ICERs) of £342,474 and £411,574, respectively. Scenario analyses exploring alternative parametric forms provided incremental discounted life-year estimates that ranged from +0.15 to +0.33 for the PartSA approach, compared with -0.13 to +0.23 for the MSM approach. This variation was reflected in the range of ICERs. The PartSA produced ICERs between £234,829 and £522,963, whereas MSM results were more variable and included instances where the intervention was dominated and ICERs above £7 million (caused by very small incremental QALYs). LIMITATIONS AND CONCLUSIONS: Structural uncertainty in economic modelling is rarely explored due to time and resource limitations. This comparison of structural approaches indicates that the choice of structure may have a profound impact on cost-effectiveness results. This highlights the importance of carefully considered model conceptualization, and the need for further research to ascertain when it may be most appropriate to use each approach.

Cost-Effectiveness Analysis of Recombinant Factor VIII Fc-Fusion Protein (rFVIIIFc) for the Treatment of Severe Hemophilia A in Italy Incorporating Real-World Dosing and Joint Health Data.

BACKGROUND: Patients with severe hemophilia A (SHA) in Italy are routinely treated with standard half-life recombinant factor VIII (rFVIII) products. rFVIII Fc-fusion protein (rFVIIIFc) is an extended half-life rFVIII product that enables less frequent administration than rFVIII, which may support improved adherence. Available data indicate low breakthrough bleed rates and potentially improved long-term joint health for patients treated with rFVIIIFc prophylaxis. OBJECTIVE: This study assessed the cost effectiveness of rFVIIIFc versus rFVIII from an Italian healthcare perspective. METHODS: A Semi-Markov model was constructed to assess the lifetime costs and benefits of rFVIII and rFVIIIFc prophylaxis. rFVIII product acquisition costs from a published Italian database were included for both prophylaxis and the resolution of breakthrough bleeding. Clinical outcomes within the model were determined based on published annualized bleeding rates and literature regarding the development of target joints (TJs) as the incidence of bleeds and TJs is associated with impaired health-related quality of life. Cost effectiveness was assessed using cost per quality-adjusted life-year (QALY) gained. RESULTS: Compared with rFVIII, rFVIIIFc was associated with a per-patient cost saving of approximately €1.3 million and QALY gains of 0.39 over a lifetime horizon. Sensitivity analyses considering alternative efficacy, dosing, and structural assumptions each showed that rFVIIIFc dominated rFVIII (i.e., provided more QALYs at a reduced cost). CONCLUSIONS: This cost-effectiveness analysis demonstrated that rFVIIIFc may offer a cost-effective treatment option for patients with SHA in Italy.

Voretigene Neparvovec for Treating Inherited Retinal Dystrophies Caused by RPE65 Gene Mutations: An Evidence Review Group Perspective of a NICE Highly Specialised Technology Appraisal.

The UK National Institute for Health and Care Excellence (NICE) considered evidence for voretigene neparvovec (VN; Luxturna®) for the treatment of RPE65-mediated inherited retinal dystrophies (IRD) within its highly specialised technology programme. This paper summarises the evidence provided by the company; the appraisal of the evidence by the Peninsula Technology Appraisal Group, who were commissioned to act as the independent evidence review group (ERG); and the development of the NICE guidance by the appraisal committee. The evidence presented by the company highlighted the significant lifelong burden of IRD for patients and carers. Evidence to support the effectiveness of VN was lacking, but the available evidence showed a modest, sustained improvement across a variety of vision-related outcomes. While patients would remain visually impaired, the committee considered that VN would prevent further deterioration in vision. The modelling approach used by the company had a number of limitations and relied heavily upon a large volume of clinical expert input to produce cost-effectiveness estimates with large uncertainty around long-term effectiveness. The ERG's main concerns revolved around these long-term outcomes and the plausibility of utility values. The NICE committee were convinced that the clinical benefits of VN were important and an appropriate use of national health service resources within a specialised service. The committee concluded that a high unmet need existed in patients with RPE65-mediated IRD and that VN represents a step change in the management of this condition.

A Review of Recent Decision-Analytic Models Used to Evaluate the Economic Value of Cancer Treatments.

Cost-effectiveness analysis provides information on the potential value of new cancer treatments, which is particularly pertinent for decision makers as demand for treatment grows while healthcare budgets remain fixed. A range of decision-analytic modelling approaches can be used to estimate cost effectiveness. This study summarises the key modelling approaches considered in oncology, alongside their advantages and limitations. A review was conducted to identify single technology appraisals (STAs) submitted to the National Institute for Health and Care Excellence (NICE) and published papers reporting full economic evaluations of cancer treatments published within the last 5 years. The review was supplemented with the existing methods literature discussing cancer modelling. In total, 100 NICE STAs and 124 published studies were included. Partitioned-survival analysis (n = 54) and discrete-time state transition structures (n = 41) were the main structures submitted to NICE. Conversely, the published studies reported greater use of discrete-time state transition models (n = 102). Limited justification of model structure was provided by authors, despite an awareness in the existing literature that the model structure should be considered thoroughly and can greatly influence cost-effectiveness results. Justification for the choice of model structure was limited and studies would be improved with a thorough rationale for this choice. The strengths and weaknesses of each approach should be considered by future researchers. Alternative methods (such as multi-state modelling) are likely to be utilised more frequently in the future, and so justification of these more advanced methods is paramount to their acceptability to inform healthcare decision making.