PharmacoEconomics最新文献

Background: Although innovation generally provides measurable improvements in disease characteristics and patient survival, some benefits can remain unclear. This study aimed to investigate patient and healthcare provider (HCP) preferences for the innovative attributes of multiple myeloma (MM) treatments.

Methods: A cross-sectional, web-based, discrete choice experiment (DCE) survey was conducted among 200 patients with MM and 30 HCPs of patients with MM in the USA. A literature review, followed by interviews with patients with MM and HCPs, was undertaken to select five attributes (progression-free survival [PFS], chance of severe side effects, how patients live with MM treatments, scientific innovation, and monthly out-of-pocket [OOP] cost) and their levels. A Bayesian efficient design was used to generate DCE choice sets. Each choice set comprised two hypothetical MM treatment alternatives described by the selected attributes and their levels. Each patient and HCP was asked to choose a preferred alternative from each of the 11 choice sets. Mixed logit and latent class models were developed to estimate patient and HCP preferences for the treatment attributes.

Results: Overall, patients and HCPs preferred increased PFS, less chance of severe side effects, a treatment that offered life without treatment, scientific innovation, and lower OOP cost. From patients' perspectives, PFS had the highest conditional relative importance (44.7%), followed by how patients live with MM treatments (21.6%) and scientific innovation (16.0%).

Conclusions: In addition to PFS, patients and HCPs also valued innovative MM treatments that allowed them to live without treatments and/or offered scientific innovation. These attributes should be considered when evaluating MM treatments.

Background: Guidelines for modelling in economic evaluation recommend that it may be necessary to consider costs and outcomes until all modelled patients have died. Some guidelines also recommend that carers' health-related quality of life (HRQoL) outcomes should be included. However, it is unclear whether economic evaluations should continue to include carers' HRQoL after patients have died, and whether there is any evidence to support an additional bereavement effect for carers.

Methods: We used the UK Household Longitudinal Study waves 1-12. We used Difference-in-Differences to estimate the short- and long-term bereavement effects on the SF-6D for people who reported that they did and did not provide care to a household member who then died. We assumed parallel trends conditional on age, sex, long-term health conditions, education, and household income.

Results: Carers and non-carers experienced a significant loss in HRQoL in the year immediately following bereavement. Carers potentially experienced a loss in HRQoL in the year before bereavement, whereas the bereavement effect may have lasted longer for non-carers. For both groups, HRQoL became comparable to the non-bereaved population around 3 years after bereavement.

Conclusions: Bereavement has a statistically significant negative impact on HRQoL in the short-term, for both carers and non-carers. However, the effect size is small and is not sustained, suggesting that including bereavement in economic evaluation would make little difference to results.

Background: The National Institute for Health and Care Excellence (NICE) may approve health technologies on condition of more evidence generated only in research (OiR) or only with research (OwR). NICE specifies the information needed to comply with its request, although it may not necessarily guarantee good quality and timely evidence for re-appraisal, before reaching a final decision.

Aim: This study aimed to critically appraise the methods, quality and risk of bias of evidence generated in response to NICE OiR and OwR technology appraisal (TA) and highly specialised technologies (HSTs) recommendations.

Methods: NICE TAs (between March 2000 and September 2020) and HST evaluations (to October 2023) of medicines were reviewed. Conditional recommendations were analysed to identify the evidence requested by NICE for re-appraisal. The new evidence was analysed for compliance with NICE's request and assessed using the Cochrane Collaboration's tools for risk of bias in randomised trials and the ROBINS-I tool for non-randomised evidence.

Results: NICE made 54 conditional recommendations from TAs (13 OiR and 41 OwR) and five conditional recommendations for HSTs (all OwR). Of these, 16 TAs presented additional evidence for re-appraisal (9 OiR [69%] and 7 OwR [17%]) and three HSTs (3 OwR [60%]). Two of the nine re-appraised TAs with OiR recommendation and four of the seven OwR complied fully with NICE's request for further evidence, while all three from the HSTs complied. The majority of re-appraised TAs and HSTs included evidence that was deemed to be at serious, high, moderate or unclear risk of bias. Among the 26 randomised controlled trials from TAs assessed, eight were categorised as having low risk of bias in all domains and ten had at least one domain as a high risk of bias. Reporting was unclear for the remainder. Twenty-two non-randomised studies, primarily single-arm studies, were susceptible to biases mostly due to the selection of participants and to confounding. Two HSTs provided evidence from randomised controlled trials which were classified as unclear or high risk of bias. All non-randomised evidence from HSTs were categorised as moderate or serious risk of bias.

Conclusions: There is widespread non-compliance with agreed data requests and important variation in the quality of evidence submitted in response to NICE conditional approval recommendations. Quality standards ought to be stipulated in respect to evidence contributing to re-appraisals following NICE conditional approval recommendations.

Objective: Chimeric antigen-receptor T-cell therapy (CAR-T) is characterised by early phase data at the time of registration, high upfront cost and a complex manufacturing and administration process compared with standard therapies. Our objective was to compare the performance of different models to assess the cost effectiveness of CAR-T using a state-transition model (STM), partitioned survival model (PSM) and discrete event simulation (DES).

Methods: Individual data for tisagenlecleucel for the treatment of young patients with acute lymphoblastic leukaemia (ALL) were used to populate the models. Costs and benefits were measured over a lifetime to generate a cost per quality-adjusted life-year (QALY). Model performance was compared quantitatively on the outcomes generated and a checklist developed summarising the components captured by each model type relevant to assessing cost effectiveness of CAR-T.

Results: Models generated similar results with base-case analyses ranging from an incremental cost per QALY of $96,074-$99,625. DES was the only model to specifically capture CAR-T wait time, demonstrating a substantial loss of benefit of CAR-T with increased wait time.

Conclusion: Although model type did not meaningfully impact base-case results, the ability to incorporate an outcome-based payment arrangement (OBA) and wait time are important elements to consider when selecting a model for CAR-T. DES provided greater flexibility compared with STM and PSM approaches to deal with the complex manufacturing and administration process that can lead to extended wait times and substantially reduce the benefit of CAR-T. This is an important consideration when selecting a model type for CAR-T, so major drivers of uncertainty are considered in funding decisions.

Background and objective: Accurately extrapolating survival beyond trial follow-up is essential in a health technology assessment where model choice often substantially impacts estimates of clinical and cost effectiveness. Evidence suggests standard parametric models often provide poor fits to long-term data from immuno-oncology trials. Palmer et al. developed an algorithm to aid the selection of more flexible survival models for these interventions. We assess the usability of the algorithm, identify areas for improvement and evaluate whether it effectively identifies models capable of accurate extrapolation.

Methods: We applied the Palmer algorithm to the CheckMate-649 trial, which investigated nivolumab plus chemotherapy versus chemotherapy alone in patients with gastroesophageal adenocarcinoma. We evaluated the algorithm's performance by comparing survival estimates from identified models using the 12-month data cut to survival observed in the 48-month data cut.

Results: The Palmer algorithm offers a systematic procedure for model selection, encouraging detailed analyses and ensuring that crucial stages in the selection process are not overlooked. In our study, a range of models were identified as potentially appropriate for extrapolating survival, but only flexible parametric non-mixture cure models provided extrapolations that were plausible and accurately predicted subsequently observed survival. The algorithm could be improved with minor additions around the specification of hazard plots and setting out plausibility criteria.

Conclusions: The Palmer algorithm provides a systematic framework for identifying suitable survival models, and for defining plausibility criteria for extrapolation validity. Using the algorithm ensures that model selection is based on explicit justification and evidence, which could reduce discordance in health technology appraisals.

Care as usual has failed to stem the tide of mental health challenges in children and young people. Transformed models of care and prevention are required, including targeting the social determinants of mental health. Robust economic evidence is crucial to guide investment towards prioritised interventions that are effective and cost-effective to optimise health outcomes and ensure value for money. Mental healthcare and prevention exhibit the characteristics of complex dynamic systems, yet dynamic simulation modelling has to date only rarely been used to conduct economic evaluation in this area. This article proposes an integrated decision-making and planning framework for mental health that includes system dynamics modelling, cost-effectiveness analysis, and participatory model-building methods, in a circular process that is constantly reviewed and updated in a 'living model' ecosystem. We describe a case study of this approach for mental health system policy and planning that synergises the unique attributes of a system dynamics approach within the context of economic evaluation. This kind of approach can help decision makers make the most of precious, limited resources in healthcare. The application of modelling to organise and enable better responses to the youth mental health crisis offers positive benefits for individuals and their families, as well as for taxpayers.

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.

Background and objective: China has the highest number of hepatitis C virus (HCV) infections in the world. However, it is unclear what levels of screening and treatment are needed to achieve the WHO 2030 hepatitis C elimination targets. We aimed to evaluate the impact of scaling up interventions on the hepatitis C epidemic and determine how and at what cost these elimination targets could be achieved for the whole population in China.

Methods: We developed a compartmental model incorporating HCV transmission, disease progression, and care cascade for the whole population in China, calibrated with data on demographics, injecting drug use, HCV prevalence, and treatments. Five different scenarios were evaluated for effects and costs for 2022-2030. All costs were converted to 2021 US dollar (USD) and discounted at an annual rate of 5%. One-way sensitivity analyses were conducted to assess the robustness of the model.

Results: Under the status quo scenario, the incidence of hepatitis C is projected to increase from 60.39 (57.60-63.45) per 100,000 person-years in 2022 to 68.72 (65.3-73.97) per 100,000 person-years in 2030, and 2.52 million (1.94-3.07 million) infected patients are projected to die between 2022 and 2030, of which 0.76 (0.61-1.08) million will die due to hepatitis C. By increasing primary screening to 10%, conducting regular rescreening (annually for PWID and every 5 years for the general population) and treating 90% of patients diagnosed, the incidence would be reduced by 88.15% (86.61-89.45%) and hepatitis C-related mortality by 60.5% (52.62-65.54%) by 2030, compared with 2015 levels. This strategy would cost USD 52.78 (USD 43.93-58.53) billion.

Conclusions: Without changes in HCV prevention and control policy, the disease burden of HCV in China will increase dramatically. To achieve the hepatitis C elimination targets, China needs to sufficiently scale up screening and treatment.

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.