PharmacoEconomics最新文献

Introduction: When developing health economic simulation models, individual-level and cohort state-transition model types are commonly used. However, heterogeneity and the extent to which it is taken into account is thought to affect simulation outcomes differently in individual-level and cohort simulations, even when model structures are identical.

Objective: This study aimed to investigate the conditions under which the use of different model types may lead to different outcomes and therefore potentially different policy decisions.

Methods: A microsimulation model was used to reflect an individual-level simulation, simulating patient characteristics and, artificially, a cohort-level simulation of identical patients, using the exact same model structure. Four scenarios were analyzed: heterogeneity in age (scenario 1) influencing progression and recovery probabilities when on treatment, heterogeneity in sex (scenario 2) influencing progression and recovery probabilities when on treatment, combined heterogeneity in age and sex (scenario 3) influencing progression and recovery probabilities when on treatment, and heterogeneity in age when including age-dependent all-cause mortality (scenario 4). In every scenario, heterogeneity impact was varied, and health state occupancy, incremental costs, incremental effects, and the net monetary benefit of treatment versus no treatment were compared between the individual-level and cohort simulations.

Results: When introducing heterogeneity in age, sex, and age and sex combined, all scenarios showed differences between outcomes of individual-level and cohort simulations. However, these differences did not change the cost-effectiveness conclusions. When age influenced only age-dependent mortality, there were differences between the outcomes for the individual-level and cohort simulations when heterogeneity in age was introduced.

Conclusion: Patient heterogeneity can affect the outcomes of individual and cohort simulations differently, but reflecting more heterogeneity does not necessarily increase differences in simulation outcomes. However, age-dependent mortality affected analytic outcomes differently, suggesting a need for caution when developing cohort models if age is heterogeneous.

Background: Cure models are becoming more popular for modelling survival data where long-term survival, or 'cure', is considered plausible. One criterion for considering fitting cure models is evidence for a plateau in the Kaplan-Meier survival curve. However, what constitutes a mathematical definition of a plateau in survival probability is unclear, and visual inspections of survival curves are subjective.

Objective: We investigate these issues and clarify what is meant by a plateau in this context.

Methods: We begin by describing an activity where five experts were presented with 10 survival curves from oncology trials. They were asked to rank these curves in order of their potential suitability for mixture cure modelling. We explore mathematically what features of data are required to produce a positive estimated cure fraction under an exponential mixture cure model. We show how these results can be generalised to a Weibull mixture cure model. A case study was performed using one of the survival curves.

Results: We found weak correlations between the experts' rankings. Mathematical investigations revealed the features of data required for mixture cure models to be potentially useful, such as a decreasing event rate, but this is highly model dependent. The case study illustrated similar statistical issues.

Conclusions: We conclude that a precise definition of the extent to which a Kaplan-Meier survival curve demonstrates a plateau is likely to prove elusive. External evidence or subject matter expert knowledge about the plausibility of cure must therefore play a key role.

Cohort-level models in Microsoft Excel^® remain the standard for cost-effectiveness modeling to inform health technology assessment (HTA), despite calls and rationale for more flexible approaches. Their limited ability to capture patient-level characteristics can, in the presence of patient heterogeneity or the need to track patient characteristics to accurately capture a technology's implications, introduce bias. Their continued prevalence is explained by key stakeholders' familiarity with spreadsheet software, and the lower computational burden of cohort-level versus patient-level models. However, contemporary Excel functions have opened up possibilities for calculations within native Excel that enable more flexible, patient-level approaches to be implemented in familiar spreadsheet-based software, without use of any Visual Basic for Applications (VBA) code. Therefore, this tutorial aims to provide step-by-step guidance on how to implement a previously published and freely available individual-level discrete event simulation (DES) in Excel, using contemporary Excel functions and without any VBA code.

Background: The choice of perspective in valuation tasks is likely to affect the scale of EQ-5D-Y-3L value sets, but less is known about how it affects the relative importance of different dimensions.

Objectives: The aim of this study was to examine how preferences for EQ-5D-Y-3L health states differ according to different perspectives utilising two methods: the Online elicitation of Personal Utility Functions (OPUF) tool and a discrete choice experiment (DCE).

Methods: An online survey was designed containing the OPUF tool and a DCE. Adult respondents from the United Kingdom were randomised to one of five different perspective arms: (1) 4-year-old child, (2) 10-year-old child, (3) a child of unspecified age, (4) another adult, and (5) own health. The resulting OPUF value sets (social utility functions), and relative importance scores for the five dimensions from both methods, were compared across perspectives.

Results: Results differed by perspective in both valuation tasks. In both tasks, 'looking after myself' was less important and 'pain or discomfort' was more important in the child perspectives than in the adult perspectives. Furthermore, the scale of the value sets produced by the OPUF tool differed by perspective, with the value of the worst health state being significantly lower in the adult perspectives than in the child perspectives.

Conclusion: Our results suggest that the valuation of the EQ-5D-Y-3L is affected by the perspective that adult respondents are asked to take. Researchers should be aware of the potential impact and ensure that relevant stakeholders understand this when designing valuation studies.

The cost effectiveness of pharmacotherapies for obesity (such as semaglutide, tirzepatide, liraglutide, and newer agents) is increasingly being appraised by health technology assessment (HTA) bodies. Modelling is required to extrapolate weight change observed over relatively short clinical trial durations to long-term weight loss and associated cardio-metabolic outcomes and costs. Extrapolation is a common issue in HTA, but there is a unique challenge for anti-obesity drugs because of the number of interacting uncertainties. This is a particular concern given the substantial eligible population sizes and associated high financial decision risk of providing lifetime treatment. We describe four key challenges in modelling pharmacotherapies for obesity: (1) modelling long-term body mass index (BMI) trajectories with and without obesity pharmacotherapy, (2) modelling time on treatment, (3) using risk equations to link changes in BMI to clinical outcomes, and (4) modelling clinical outcomes not (solely) related to BMI changes. We discuss each of these challenges and the impact they have had in global HTA appraisals for pharmacotherapies. We speculate how these challenges relating to short-term clinical trials could be overcome to more robustly predict long-term outcomes and the role that observational data may play. As clinical trial and real-world evidence for technologies for obesity evolves, analysts and decision-makers need to determine which evidence sources are most appropriate and how they should be combined.

Background: Randomized controlled trials are the standard for health technology assessment, but when they are infeasible or unethical, single-arm trials (SATs) are submitted.

Objectives: This study examined when SATs were accepted for added benefit by the Institute for Quality and Efficiency in Health Care (IQWiG) and/or the Federal Joint Committee (G-BA) in Germany.

Methods: We identified health technology assessments via the AMNOG-Monitor database through December 2024, with additional details from G-BA documents. We compared the SATs and other evidence for added benefit decisions (granted/not granted), stratified by orphan drug status, special marketing authorization, approved indication (chronic hepatitis C/others), and population (adults/children). Added benefit claims by manufacturers, IQWiG recommendations, and G-BA appraisals were compared.

Results: Among 1738 G-BA decisions, 85.8% (1491/1738) of the subpopulations were fully assessed by IQWiG, with 13.5% (202/1491) based on SATs. Among the 247 orphan drugs assessed by the G-BA, 37.7% (93/247) were SAT-based. Overall, SAT-based assessments demonstrated an added benefit in 12.2% (36/295) of cases. This included 13.4% (27/202) of full assessments and 9.7% (9/93) of orphan drug assessments. IQWiG accepted only 18.5% (5/27) of the SATs endorsed by the G-BA. Statistical tests revealed significant differences between manufacturers' claims, IQWiG recommendations, and G-BA appraisals. SATs were most frequently accepted for chronic hepatitis C treatments (mostly with non-standard marketing authorization) and paediatric indications. The G-BA cited reasons such as dramatic effects, rare diseases, a lack of alternatives, or fewer side effects, although justifications were often unclear.

Conclusion: Acceptance rates for SATs remain low, and criteria for added benefit are not always explicitly defined. To enable benefit assessments when randomised controlled trials are infeasible or unethical, clear and binding criteria developed in collaboration with the G-BA are essential.

Objective: To what extent a care pathway, due to its associated pollution, may be more detrimental to future health than beneficial to contemporary patients is still an open question. We present a methodological framework to integrate pollutant-induced future health damages in health technology assessment (HTA) metrics like quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs) for a better evaluation of the cost effectiveness of care pathways.

Methods: We used the ReCiPe model to estimate the future detrimental health impact (in disability-adjusted life years [DALY]) of pollutants from the US healthcare system, showing the major impact of GHG emissions compared with other pollutants. An adapted version of the ReCiPe model was used to convert GHG emissions from care pathways into future DALY_GHG, QALY_GHG, and life years (LY_GHG), as well as the associated confidence intervals. For a given care pathway, future health damages were compared with patient benefits (e.g., QALY_GHG/QALY_patient). Damages may also be integrated in the ICER_GHG by subtracting future health losses from patient health benefits. Case applications are provided.

Results: Future damages to health emerging from pollutants emitted by the US healthcare system were estimated at 7,363,000 DALYs per year. Focusing on GHG emissions to estimate pollutant impact is reasonable, as they represent >90% of future damages. We provide estimates to convert GHG emissions into future health damages in DALY, QALY, or LY (and associated uncertainty), taking into account future impacts over different time horizons (20, 100, or 500-1000 years) and using different discount rates for future health impact (0 or 3%). We recommend estimating future damages using an egalitarian perspective (with a 0% discount rate) to maintain intergenerational equity. The QALY_GHG/QALY_patient ratio allows weighting future detrimental effects of care pathways against their benefits. For health economic evaluations, we recommend integrating GHG emissions into the ICER, preferably in its denominator (QALY, DALY, LY). When focusing on specific care pathways, health gains may be substantially limited by future GHG-related detrimental impacts, especially for chronic treatments in low-risk populations. Some care pathways, like influenza vaccination, improve patient health while mitigating GHG. Accounting for GHG emissions may substantially favor or penalize one strategy over another in terms of ICER. Confidence intervals of the results were wide due to large uncertainties regarding long-term predictions.

Conclusion: HTA should consider care pathways' impact on future health to better assess the impact and cost effectiveness of health technologies. Under the hypothesis of intergenerational equity, GHG accounting has a substantial

Background and objective: Informal caregivers play a critical role in supporting individuals with musculoskeletal conditions. This systematic review aimed to evaluate the psychological and economic burdens associated with caregiving for musculoskeletal conditions.

Methods: We conducted a systematic review in accordance with PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines, searching MEDLINE, Embase, CINAHL, EconLIT, and APA PsycINFO for studies published between 2000 and April 2025. Studies were eligible if they examined the psychological and economic burden of informal caregiving for adults with musculoskeletal conditions. Screening and data extraction were conducted using EndNote 21 and Covidence. Risk of bias was assessed using the CASP checklist for psychological burden studies and the EVERS criteria for economic burden studies. Data were synthesized narratively. An exploratory meta-analysis of informal care hours was conducted using a subset of studies with sufficient statistical data.

Results: A total of 41 studies were included, with 24 reporting psychological burdens, 16 economic burdens, and one for both. Caregiving burden included emotional, social, financial, and time-related impacts, impacting the caregivers' quality of life. Higher anxiety and depression were correlated with a greater caregiver burden. Informal care costs varied by musculoskeletal condition type, location, severity, intensity, and valuation method. Reported informal care hours showed substantial variation across studies. The overall risk of bias across included studies was low.

Conclusions: This systematic review highlights the considerable psychological, economic, and time-related burdens faced by informal caregivers of individuals with musculoskeletal conditions. Caregivers face high stress, physical strain, and opportunity costs. The lack of standardized assessments hinders accurate burden quantification, economic evaluation, and policy responses. Future efforts should focus on adopting consistent measurement instruments and valuation methods, alongside implementing structured policies, financial support, and psychological interventions to better support the caregivers.

Background: Developing health economic decision-analytic models requires making modelling choices to simplify reality while addressing the decision context. Finding the right balance between a decision-analytic model's simplicity and its adequacy is important but can be challenging.

Objective: We aimed to develop a tool that supports the systematic reporting and justification of modelling choices in a decision-analytic model, ensuring it is adequate and only as complex as necessary for addressing the decision context.

Methods: We identified decision-analytic model features from the key literature and our expertise. For each feature, we defined both simple and complex modelling choices that could be selected, and the consequences of simplifying a feature contrary to requirements of the decision context. Next, we designed the tool and assessed its clarity and completeness through interviews and expert workshops. To ensure consistency of use, we developed a glossary sheet and applied the tool in an illustrative case: a decision-analytic model on a repurposed drug for treatment-resistant hypertension.

Results: We conducted five interviews and two workshops with 18 decision-analytic model experts. The developed SMART (Systematic Model adequacy Assessment and Reporting Tool) consists of a framework of 28 model features, allowing users to select modelling choices per feature, then assessing the consequences of their choices for validity and transparency. SMART also includes a glossary sheet. The treatment resistant hypertension case example is provided separately.

Conclusions: SMART supports decision-analytic model development and assessment, by promoting clear reporting and justification of modelling choices, and highlighting their consequences for model validity and transparency. Thoughtful and well-justified modelling choices can help optimise the use of resources and time for model development, while ensuring the model is adequate to support decision making.