BackgroundThis study assessed the cost-effectiveness of indacaterol/glycopyrronium (IND/GLY) versus salmeterol/fluticasone (SFC) in chronic obstructive pulmonary disease (COPD) patients with moderate to very severe airflow limitation and ≥1 exacerbation in the preceding year.MethodsA previously published and validated patient-level simulation model was adapted using clinical data from the FLAME trial and real-world cost data from the ARCTIC study. Costs (total monetary costs comprising drug, maintenance, exacerbation, and pneumonia costs) and health outcomes (life-years (LYs), quality-adjusted life-years (QALYs)) were projected over various time horizons (1, 5, 10 years, and lifetime) from the Swedish payer’s perspective and were discounted at 3% annually. Uncertainty in model input values was studied through one-way and probabilistic sensitivity analyses. Subgroup analyses were also performed.ResultsIND/GLY was associated with lower costs and better outcomes compared with SFC over all the analysed time horizons. Use of IND/GLY resulted in additional 0.192 LYs and 0.134 QALYs with cost savings of €1211 compared with SFC over lifetime. The net monetary benefit (NMB) was estimated to be €8560 based on a willingness-to-pay threshold of €55,000/QALY. The NMB was higher in the following subgroups: severe (GOLD 3), high risk and more symptoms (GOLD D), females, and current smokers.ConclusionIND/GLY is a cost-effective treatment compared with SFC in COPD patients with mMRC dyspnea grade ≥ 2, moderate to very severe airflow limitation, and ≥1 exacerbation in the preceding year.Electronic supplementary materialThe online version of this article (10.1186/s12931-017-0688-5) contains supplementary material, which is available to authorized users.
Costs and utilities are key inputs into any cost-effectiveness analysis (CEA). Their estimates are typically derived from individual patient level data collected as part of clinical studies whose follow up duration is often too short to allow a robust quantification of the likely costs and benefits a technology will yield over the entire patient's lifetime.In the absence of long-term data, some form of temporal extrapolation -to project short-term evidence over a longer time horizon -will be required. Temporal extrapolation inevitably involves assumptions regarding the behaviour of the quantities of interest beyond the time horizon supported by the clinical evidence.Unfortunately, the implications for decisions made on the basis of evidence derived following this practice and the degree of uncertainty surrounding the validity of any assumptions made are often not fully appreciated.The issue is compounded by the absence of methodological guidance concerning the extrapolation of nontime to event outcomes such as costs and utilities. This paper considers current approaches to predict long-term costs and utilities, highlights some of the challenges with the existing methods, and provides recommendations for future applications. It finds that, typically, economic evaluation models employ a simplistic approach to temporal extrapolation of costs and utilities. For instance, their parameters (e.g. mean) are typically assumed to be homogeneous with respect to both time and patients' characteristics. Furthermore, costs and utilities have often been modelled to follow the dynamics of the associated time-to-event outcomes. However, cost and utility estimates may be more nuanced, and it is important to ensure extrapolation is carried out appropriately for these parameters. Key points1. Need to consider how costs and utilities are extrapolated in a cost-effectiveness model 2. There is no guidance on methods for extrapolating costs and utilities 3. Existing applications use various approaches with little justification
Background: For novel migraine therapies, economic evaluations will be required to understand the trade-offs between additional health benefit and additional cost. The purpose of this study was to conduct a systematic literature review (SLR) to identify previous economic evaluations in migraine from the United Kingdom or Irish perspective to critically appraise these evaluations and to propose, if necessary, a novel modelling approach that can be used for future economic evaluations of migraine therapies. Methods: An SLR was conducted to identify previous economic evaluations of preventive migraine treatments. Key opinion leaders were consulted to determine the criteria for a robust migraine economic evaluation. Economic evaluations identified in the SLR were appraised against these criteria, and a novel cost-effectiveness model structure was then proposed. Results: Eight records reporting on published economic evaluations were identified and critically appraised for general quality. Expert consultation provided 6 recommendations on the ideal model structure for migraine that is both clinically and economically meaningful. A decision-tree plus Markov structure was then developed as a cost-effectiveness model for migraine therapies where each health state is associated with a patient distribution across monthly migraine day (MMD) frequencies. Conclusions: Future migraine economic evaluations should allow for assessments across the full spectrum of migraine, a response-based stopping rule, and the estimation of benefits and resource costs based on MMD frequency. The approach proposed in this paper captures all of the desired elements for an economic evaluation of migraine therapy and is suitable to assess new migraine therapies.
OBJECTIVES: Since the required time horizon in a cost effectiveness decision model often exceeds the evidence time horizon, numerous temporal uncertainties arise regarding model parameters and structures. The objective of this study is to demonstrate, through a motivating example: (i) why temporal uncertainty ought to be addressed more thoroughly than it has been to date; (ii) how this uncertainty might be expressed in decision models; and (iii) the consequences for the costeffectiveness results when temporal uncertainty is incorporated into the analysis. METHODS: Taking the example of a decision model seeking to estimate the costeffectiveness of an early interventional strategy for patients with non-ST-elevation acute coronary syndrome, we firstly highlight the model components that are exposed to temporal uncertainty. Focusing on two key model parameters, we explore the extent to which the existing short-term evidence could reasonably be extrapolated over time. We then suggest a means to quantitatively convey the temporal uncertainty pertaining to these parameters within the model. RESULTS: Temporal uncertainty is shown to have a significant impact on the cost-effectiveness results. Value-of-Information analysis (specifically population EVPPI) suggests that for this example, it may have been more cost-effective to delay adoption recommendation until further evidence on the temporal behaviour of parameters was collected. CONCLUSIONS: Temporal uncertainty, though rarely formally modelled, is a significant characteristic of cost-effectiveness decision models. It is possible and desirable to express temporal uncertainty within a decision model, as the complete model may show that it is more cost-effective to collect further information on the temporal behaviour of model parameters before issuing an adoption recommendation.
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