PharmacoEconomics最新文献

We propose a short-cut heuristic approach to rapidly estimate value of information (VOI) using information commonly reported in a research funding application to make a case for the need for further evaluative research. We develop a "Rapid VOI" approach, which focuses on uncertainty in the primary outcome of clinical effectiveness and uses this to explore the health consequences of decision uncertainty. We develop a freely accessible online tool, Rapid Assessment of the Need for Evidence (RANE), to allow for the efficient computation of the value of research. As a case study, the method was applied to a proposal for research on shoulder pain rehabilitation. The analysis was included as part of a successful application for research funding to the UK National Institute for Health and Care Research. Our approach enables research funders and applicants to rapidly estimate the value of proposed research. Rapid VOI relies on information that is readily available and reported in research funding applications. Rapid VOI supports research prioritisation and commissioning decisions where there is insufficient time and resources available to develop and validate complex decision-analytic models. The method provides a practical means for implementing VOI in practice, thus providing a starting point for deliberation and contributing to the transparency and accountability of research prioritisation decisions.

Background and objective: Multiple myeloma is a rare incurable hematological cancer in which most patients relapse or become refractory to treatment. This systematic literature review aimed to critically review the existing economic models used in economic evaluations of systemic treatments for relapsed/refractory multiple myeloma and to summarize how the models addressed differences in the line of therapy and exposure to prior treatment.

Methods: Following a pre-approved protocol, literature searches were conducted on 17 February, 2023, in relevant databases for models published since 2014. Additionally, key health technology assessment agency websites were manually searched for models published as part of submission dossiers since 2018. Reported information related to model conceptualization, structure, uncertainty, validation, and transparency were extracted into a pre-defined extraction sheet.

Results: In total, 49 models assessing a wide range of interventions across multiple lines of therapy were included. Only five models specific to heavily pre-treated patients and/or those who were refractory to multiple treatment classes were identified. Most models followed a conventional simple methodology, such as partitioned survival (n = 28) or Markov models (n = 9). All included models evaluated specific interventions rather than the whole treatment sequence. Where subsequent therapies were included in the model, these were generally only considered from a cost and resource use perspective. The models generally used overall and progression-free survival as model inputs, although data were often immature. Sensitivity analyses were frequently reported (n = 41) whereas validation was only considered in less than half (n = 19) of the models.

Conclusions: Published economic models in relapsed/refractory multiple myeloma rarely followed an individual patient approach, mainly owing to the higher need for complex data assumptions compared with simpler modeling approaches. As many patients experience disease progression on multiple treatment lines, there is a growing need for modeling complex treatment strategies, leading to more sophisticated approaches in the future. Maintaining transparency, high reporting standards, and thorough analyses of uncertainty are crucial to support these advancements.

Background: Amyotrophic lateral sclerosis (ALS) is a devastating disease which leads to loss of muscle function and paralysis. Historically, clinical drug development has been unsuccessful, but promising disease-modifying therapies (DMTs) may be on the horizon.

Objectives: The aims of this study were to estimate survival, quality-adjusted life-years (QALYs) and costs under current care, and to explore the conditions under which new therapies might be considered cost effective.

Methods: We developed a health economic model to evaluate the cost effectiveness of future ALS treatments from a UK National Health Service and Personal Social Services perspective over a lifetime horizon using data from the ALS-CarE study. Costs were valued at 2021/22 prices. Two hypothetical interventions were evaluated: a DMT which delays progression and mortality, and a symptomatic therapy which improves utility only. Sensitivity analysis was conducted to identify key drivers of cost effectiveness.

Results: Starting from King's stage 2, patients receiving current care accrue an estimated 2.27 life-years, 0.75 QALYs and lifetime costs of £68,047. Assuming a 50% reduction in progression rates and a UK-converted estimate of the price of edaravone, the incremental cost-effectiveness ratio for a new DMT versus current care is likely to exceed £735,000 per QALY gained. Symptomatic therapies may be more likely to achieve acceptable levels of cost effectiveness.

Conclusions: Regardless of efficacy, DMTs may struggle to demonstrate cost effectiveness, even at a low price. The cost effectiveness of DMTs is likely to be strongly influenced by drug price, the magnitude and durability of relative treatment effects, treatment starting/stopping rules and any additional utility benefits over current care.

Objectives: Most type 2 diabetes simulation models utilise equations mapping out lifetime trajectories of risk factors [e.g. glycated haemoglobin (HbA_1c)]. Existing equations, using historic data or assuming constant risk factors, frequently underestimate or overestimate complication rates. Updated risk factor time path equations are needed for simulation models to more accurately predict complication rates.

Aims: (1) Update United Kingdom Prospective Diabetes Study Outcomes Model (UKPDS-OM2) risk factor time path equations; (2) compare quality-adjusted life-years (QALYs) using original and updated equations; and (3) compare QALY gains for reference case simulations using different risk factor equations.

Methods: Using pooled contemporary data from two randomised trials EXSCEL and TECOS (n = 28,608), we estimated: dynamic panel models of seven continuous risk factors (high-density lipoprotein cholesterol, low density lipoprotein cholesterol, HbA_1c, haemoglobin, heart rate, blood pressure and body mass index); two-step models of estimated glomerular filtration rate; and survival analyses of peripheral arterial disease, atrial fibrillation and albuminuria. UKPDS-OM2-derived lifetime QALYs were extrapolated over 70 years using historical and the new risk factor equations.

Results: All new risk factor equation predictions were within 95% confidence intervals of observed values, displaying good agreement between observed and estimated values. Historical risk factor time path equations predicted trial participants would accrue 9.84 QALYs, increasing to 10.98 QALYs using contemporary equations.

Discussion: Incorporating updated risk factor time path equations into diabetes simulation models could give more accurate predictions of long-term health, costs, QALYs and cost-effectiveness estimates, as well as a more precise understanding of the impact of diabetes on patients' health, expenditure and quality of life.

Trial registration: ClinicalTrials.gov NCT01144338 and NCT00790205.

Introduction: The EQ-5D-Y-3L is a generic measure of health-related quality of life in children and adolescents. Although the Brazilian-Portuguese EQ-5D-Y-3L version is available, there is no value set for it, hampering its use in economic evaluations. This study aimed to elicit a Brazilian EQ-5D-Y-3L value set based on preferences of the general adult population.

Methods: Two independent samples of adults participated in an online discrete choice experiment (DCE) survey and a composite time trade-off (cTTO) face-to-face interview. The framing was "considering your views for a 10-year-old child". DCE data were analyzed using a mixed-logit model. The 243 DCE predicted values were mapped into the observed 28 cTTO values using linear and non-linear mapping approaches with and without intercept. Mapping approaches' performance was assessed to estimate the most valid method to rescale DCE predicted values using the model fit (R²), Akaike Information Criteria (AIC), root mean squared error (RMSE), and mean absolute error (MAE).

Results: A representative sample of 1376 Brazilian adults participated (DCE, 1152; cTTO, 211). The linear mapping without intercept (R² = 96%; AIC, - 44; RMSE, 0.0803; MAE, - 0.0479) outperformed the non-linear without intercept (R² = 98%; AIC, - 63; RMSE, 0.1385; MAE, - 0.1320). Utilities ranged from 1 (full health) to - 0.0059 (the worst health state). Highest weights were assigned to having pain or discomfort (pain/discomfort), followed by walking about (mobility), looking after myself (self-care), doing usual activities (usual activities), and feeling worried, sad, or unhappy (anxiety/depression).

Conclusion: This study elicited the Brazilian EQ-5D-Y-3L value set using a mixed-logit DCE model with a power parameter based on a linear mapping without intercept, which can be used to estimate the quality-adjusted life-years for economic evaluations of health technologies targeting the Brazilian youth population.

Survival extrapolation often plays an important role in health technology assessment (HTA), and there are a range of different approaches available. Approaches that can leverage external evidence (i.e. data or information collected outside the main data source of interest) may be helpful, given the extent of uncertainty often present when determining a suitable survival extrapolation. One of these methods is the multi-parameter evidence synthesis (MPES) approach, first proposed for use in HTA by Guyot et al., and more recently by Jackson. While MPES has potential benefits over conventional extrapolation approaches (such as simple or flexible parametric models), it is more computationally complex and requires use of specialist software. This tutorial presents an introduction to MPES for HTA, alongside a user-friendly, publicly available operationalisation of Guyot's original MPES that can be executed using the statistical software package R. Through two case studies, both Guyot's and Jackson's MPES approaches are explored, along with sensitivity analyses relevant to HTA. Finally, the discussion section of the tutorial details important considerations for analysts considering use of an MPES approach, along with potential further developments. MPES has not been used often in HTA, and so there are limited examples of how it has been used and perceived. However, this tutorial may aid future research efforts exploring the use of MPES further.

Objectives: Published literature has levied criticism against the cost-minimisation analysis (CMA) approach to economic evaluation over the past two decades, with multiple papers declaring its 'death'. However, since introducing the requirements for economic evaluations as part of health technology (HTA) decision-making in 1992, the cost-minimisation analysis (CMA) approach has been widely used to inform recommendations about the public subsidy of medicines in Australia. This research aimed to highlight the breadth of use of CMA in Australia and assess the influence of preconditions for the approach on subsidy recommendations METHODS: Relevant information was extracted from Public Summary Documents of Pharmaceutical Benefits Advisory Committee (PBAC) meetings in Australia considering submissions for the subsidy of medicines that included a CMA and were assessed between July 2005 and December 2022. A generalised linear model was used to explore the relationship between whether medicines were recommended and variables that reflected the primary preconditions for using CMA set out in the published PBAC Methodology Guidelines. Other control variables were selected through the Bolasso Method. Subgroup analysis was undertaken which replicated this modelling process.

Results: While the potential for inferior safety or efficacy reduced the likelihood of recommendation (p < 0.01), the effect sizes suggest that the requirements for CMA were not requisite for recommendation.

Conclusion: The Australian practice of CMA does not strictly align with the PBAC Methodology Guidelines and the theoretically appropriate application of CMA. However, within the confines of a deliberative HTA decision-making process that balances values and judgement with available evidence, this may be considered acceptable, particularly if stakeholders consider the current approach delivers sufficient clarity of process and enables patients to access medicines at an affordable cost.

Background: Psoriatic arthritis (PsA) is an inflammatory disease characterised by a variety of clinical manifestations. Considering the economic burden posed by PsA and the increasing number of treatment options, economic evaluations are required to better allocate available resources. This work aims to update a previous published literature review on PsA cost-of-illness and cost-effectiveness analysis.

Methods: A search was performed of English-language literature between January 2017 and March 20, 2024 in Medline/PubMed, Embase and Cochrane library using the terms 'psoriatic arthritis', 'cost of illness' and 'cost effectiveness'. Data on decision model, time horizon, population, treatment options, perspective, type of costs, relevant results and authors' conclusion were extracted from the reviewed articles. Finally, the quality of the included studies was evaluated.

Results: Twenty-seven studies met the inclusion criteria: 16 cost-of-illness and 11 cost-effectiveness/cost-utility analyses. PsA is characterised by high direct and indirect costs. Drug costs as well as hospitalisation and absenteeism were the major drivers of the observed costs. The cost-effectiveness analyses reported the dominance or the cost effectiveness of biologic therapies compared with non-biologic PsA treatment. Biological options like bimekizumab and ixekizumab have demonstrated a better cost-effectiveness profile in PsA patients compared with other treatments (i.e., other biological treatments).

Conclusions: There was an increased number of economic evaluations compared with the previous review. PsA is still associated with significant economic burden worldwide. The main cost was represented by therapies, specifically biological therapies. Amongst the biological therapies, bimekizumab and ixekizumab appear to provide the most economic benefit. Finally, new economic studies are needed to enrich knowledge on the economic burden of subgroups of PsA patients as well as early treatment of PsA with new therapies.