Value in Health

Background Conventional cost-effectiveness analysis assumes societal indifference to the distribution of health gains across potential patients, yet evidence suggests public preferences vary between concentrating and dispersing benefits, depending on the size of health gain. The handful of Person Trade-Off (PTO) studies that explored this did not examine the cognitive mechanisms and rationales underlying the PTO tasks. Objectives To qualitatively investigate how individuals conceptualise and justify trade-off decisions within a PTO task designed to elicit distributional preferences regarding healthcare benefits. Methods Following a pilot phase (n=10), online cognitive debrief interviews (n=10) were conducted with UK adults using a think-aloud protocol and concurrent verbal probing. Participants completed an interactive, web-based PTO task that presented choices between concentrating larger health gains among fewer recipients or dispersing smaller gains across a larger population. Data were analysed using codebook thematic analysis. Results Six primary themes emerged across two categories: decision-making processes and decision drivers. Participants displayed varying degrees of moral discomfort, utilizing cognitive mechanisms ranging from strict mathematical logic (QALY maximisation) to emotion-driven heuristics. Key decision drivers included patient age, quality of survival, societal productivity, and strong death aversion. Participants frequently rejected pure QALY maximisation and held strong egalitarian preferences to give every patient a chance to receive treatment. Conclusion Public perspectives on benefit distribution are highly heterogeneous and shaped by complex normative values rather than pure health maximisation. Understanding these qualitative rationales is essential for isolating genuine distributional preferences from cognitive shortcuts, aiding health technology assessment bodies in evaluating equity weights.

BACKGROUNDTransparency and credibility of health-economic simulation models is essential to inform reimbursement decisions. Model replication can support model transparency and credibility. Artificial intelligence (AI), particularly large language models, offers new opportunities to accelerate model replication. This led to the research question: "To what extent can the results of existing health-economic Markov models be replicated by models developed using generative AI for eliciting input parameters and code generation?" METHODSReplication was performed in three steps. First, a chain-of-thought prompting strategy in ChatGPT-4 was developed to replicate in R an open-source model co-developed by one of the authors and with publicly available code. Second, it was applied to replicate a model co-developed by one of the authors but without publicly available code. Third, it was applied to a model without the involvement of the authors and without publicly available code. A mixed- methods approach was employed in terms of qualitatively addressing the face validity of the prompt development and refinement and quantitatively assessing deviations between AI- generated and original model predictions. RESULTSThe first model required approximately one month to replicate, while adaptations to the second and third models took approximately two weeks each. Across the three models and 45 replications (15 per model), the average absolute relative deviations between ChatGPT-4 generated model predictions and published results were: [≤]14% for quality-adjusted life years and costs in the first model, [≤]7% in the second model, and [≤]28% in the third model. CONCLUSIONSOur approach could support more time-efficient model replication for reimbursement decision-makers, researchers or pharmaceutical companies. This could contribute to transparency and credibility of health-economic models.

ObjectivesGenerative AI (GenAI) has emerged in the current decade as a paradigm-shifting technology with potential to transform the process of health economic evaluation (HEE), a resource-intensive element of health technology assessment. This systematic literature review aims to identify the current applications of GenAI in HEE and its potential advantages, challenges and limitations. MethodsWe searched Medline, Embase, EconLit, Cochrane Library, International HTA database and Epistemonikos for English-language, publicly available literature without date restrictions, and hand-searched the ISPOR presentations database (2023 to 2025) for articles describing or investigating the use of GenAI in HEE. Quantitative data on performance outcomes were collected along with qualitative data on stakeholder opinions and experience. ResultsWe identified 25 eligible studies: 18 primary studies, 6 narrative reviews and 1 expert opinion piece. The primary studies comprised 16 case studies and 2 qualitative studies. Over 90% of studies were conference abstracts published in 2024 from commercial authors. The emphasis across studies was on early exploratory research, particularly model replication. Where reported, execution time (3 studies), accuracy and error rate (7 studies), and user experience (4 studies) showed promising results across multiple use cases but there is a high risk of bias inherent in relying on conference abstracts with limited reporting, which warrants cautious interpretation. ConclusionThe current evidence landscape has revealed potential benefits of GenAI across multiple applications to health economics, but only sparse dissemination of early case study findings via conference submissions. Further research is needed to validate all use cases and to address perceived barriers to implementation.

Background: Cost-effectiveness analyses (CEA) built on simulation models are usually delivered through project-specific scripts. Each new model, and often each new question, rebuilds the same scaffolding: parameter management, calibration, base-case analysis, sensitivity analysis and export. This is costly, and is where traceability between an estimate and the model version producing it is lost. We present THALASSA, a web application providing this infrastructure once, for any model satisfying a small programming contract. Methods: A simulation model is connected by one file implementing a fixed contract of R functions that determine its behaviour. THALASSA exposes the complete CEA workflow in a browser: a generated overview of the connected model, parameter editing, calibration, base-case comparison with the efficiency curve, and deterministic and probabilistic sensitivity analysis at a willingness-to-pay threshold, each with timestamped file export carrying the parameter values it was run with. Every session is bound to a repository version, so results carry their generating model version. Results: We demonstrate THALASSA on two public models of contrasting difficulty: a simple tutorial model and a generic cancer screening model with two competing natural-history pathways and a 21-dimensional calibration deliberately built to be more complex. Both ran on the same unmodified setup, implementing the same contract, with no code changes. Conclusions: Separating analysis infrastructure from model logic makes the CEA workflow reusable across projects and reproducible by construction. A modelling team can move from a versioned repository to audit-ready, exportable CEA outputs without writing analysis code. The integration effort does not grow with model size and one installation serves unrelated projects.

BackgroundDemonstrating safety and efficacy of new medical treatments requires clinical trials. But clinical trials are costly and may not provide value proportionate to their costs. In health systems with limited resources, it is important to identify the trials with the highest value. Tools exist to assess elements of a clinical trial such as statistical validity but are not wholistic in their valuation of a clinical trial. This study aims to develop a measure of clinical trials value and provide an online tool for clinical trial prioritisation. MethodsA search of the academic and grey literature and expert consultation was undertaken to identify a set of metrics to aid clinical trial valuation using multi-criteria decision analysis. Swing weighting and ranking exercises were used to calculate appropriate weights of each of the included metrics and to estimate the partial-value function for each underlying metric. The set of metrics and their respective weights were applied to the results of six different clinical trials to calculate their value. ResultsSeven metrics were identified: unmet need, size of target population, eligible participants can access the trial, patient outcomes, total trial cost, academic impact and use of trial results. The survey had 80 complete sets of responses (51% response rate). A trial designed to address an Unmet Need was most commonly ranked as the most important with a weight of 24.4%, followed by trials demonstrating improved Patient Outcomes with a weight of 21.2%. The value calculated for each trial allowed for their clear delineation and thus a final value ranking for each of the six trials. ConclusionWe confirmed that the use of the decision tool for valuing clinical trials is feasible and that the results are face valid based on the evaluation of six trials. A proof-of-concept applying this tool to a larger set of trials with an external validation is currently underway.

This study analyzed the effects of patent expiration on drug prices in eight countries (US, UK, Cana-da, Australia, Japan, France, Germany, and Switzerland) and the impact of these price dynamics in cost-effectiveness assessments. First, using an event study design, we showed that average prices of drugs substantially decreased eight years after patent expiration. Then, to assess the implications of this finding for cost-effectiveness assessments, a theoretical cost-effectiveness model simulated two real-world scenarios: (1) the comparator drug was a generic and the patent of the new drug expired after market entry; (2) the comparator drug was also under patent protection, but the patent expired prior to the patent of the new drug. Not accounting for genericization or patent expiration of the com-parator drug resulted in an underestimation or overestimation of the incremental cost-effectiveness ratios, respectively. Our pricing dynamic estimates can be applied to base-case analyses of cost-effectiveness models.

BackgroundPersistent fatigue is a frequent symptom in chronic medical conditions. Systematic reviews of non-pharmacological interventions for fatigue have identified interventions that are effective at reducing fatigue, but there is limited published evidence on the cost-effectiveness of these interventions. ObjectiveTo identify non-pharmacological fatigue interventions that have the potential to be cost-effective and would warrant further investigation in future cost-effectiveness studies. DesignDecision-analytic modelling with quality-of-life outcomes mapped from a systematic review and network meta-analysis of fatigue outcomes and intervention costs estimated from staff time. SettingUK National Health Service (NHS) ParticipantsPeople with persistent fatigue associated with a chronic medical condition InterventionsNon-pharmacological fatigue interventions versus usual care Primary and secondary outcome measuresNet monetary benefit from a UK NHS and Personal Social Services (PSS) perspective; quality-adjusted life years (QALYs) gained; intervention costs valued at 2022/23 prices; costs and benefits discounted at 3.5% per annum. ResultsIn the base case analysis, expected costs from the probabilistic analysis for individual and group interventions were: {pound}267 and {pound}157 for physical activity promotion, {pound}810 and {pound}485 for CBT-Fatigue, and {pound}462 and {pound}214 for mindfulness. The expected QALYs gained were similar for mindfulness and physical activity promotion (0.061 and 0.060 respectively), but lower for CBT-Fatigue (0.045). All interventions provided positive incremental net monetary benefit (INMB) versus usual care when valuing a QALY at {pound}20,000. However, since group interventions are less costly than individual ones, and we assumed equivalent clinical benefit, they are expected to provide greater INMB. These findings remained robust across different scenarios, except for CBT-Fatigue (individual) which had negative INMB in some scenarios. ConclusionsCBT-Fatigue, physical activity promotion and mindfulness interventions all demonstrated the potential to be cost-effective versus usual care. Future research is recommended to compare the cost-effectiveness of these interventions across a broad population with different chronic conditions. STRENGTHS AND LIMITATIONS OF THIS STUDYO_LIFatigue outcomes were estimated from a robust systematic review and network analysis that pooled data across studies conducted across multiple chronic conditions, with multiple sclerosis being most common. C_LIO_LIThe treatment effects for interventions delivered to groups and individuals are assumed to be similar, but this assumption was only supported by an analysis exploring separate treatment effects for group and individual CBT-Fatigue interventions. C_LIO_LIThe results for group physical activity promotion and individual mindfulness interventions should be treated with caution owing to there only being data beyond end of treatment for individual physical activity promotion and group mindfulness interventions. C_LIO_LIHealthcare costs were restricted to staffing costs for delivering the intervention and therefore do not capture any impact on resource use outside of the intervention or any non-staff intervention cost such as access to a specific digital tool. C_LI

BackgroundNational Institute for Health and Care Excellence (NICE) data regarding manufacturer-driven terminations indicate that some patients in the United Kingdom (UK) are unable to access treatments that are available in other European countries, which may result in reduced survival and quality of life (QoL). This study aims to quantify the health impact of NICE appraisals for multi-indication products terminated for reasons not related to clinical trial failure on the UK population. MethodsTerminated NICE appraisals (2014-2023) for multi-indication products were identified and a targeted literature search was conducted to identify data on the health impact of the interventions. The potential incremental quality-adjusted life year (QALY) loss and impact on overall survival (OS), progression-free survival (PFS), and QoL was calculated. ResultsOver 16,000 QALYs/year were potentially lost (with one QALY equal to one year of life in perfect health) across approximately 829,000 patients in the UK due to NICE appraisals for multi-indication products being terminated for reasons not related to clinical trial failure. Across oncology indications (approximately 18,900 patients), OS and PFS may have been reduced by over 9,400 years and 9,000 years, respectively. The potential impact of the treatments for non-oncology indications for which NICE appraisals were terminated on QoL was an incremental improvement of 13% (weighted average). ConclusionsDue to the increasing number of NICE terminations for multi-indication products, patients cannot access therapies that could lengthen their lives and increase their QoL. As the UK uniform pricing policy is likely to influence manufacturer-driven terminations, introducing alternative reimbursement arrangements such as indication-based pricing (IBP) agreements to ensure that prices remain commensurate with therapeutic value could improve access to therapies in the UK, thereby improving public health. HighlightsO_LINational Institute for Health and Care Excellence (NICE) termination data indicate that some patients in the United Kingdom (UK) are unable to access treatments available in other European countries, which could potentially prolong their lives and improve their quality of life (QoL) C_LIO_LIAcross approximately 829,000 patients in the UK, over 16,000 quality-adjusted life years (QALYs) per year (with one QALY equal to one year of life in perfect health) are potentially lost as a result of NICE appraisals for multi-indication products that have been terminated for reasons not related to clinical trial failure C_LIO_LIAssessing reimbursement options such as indication-based pricing (IBP) agreements for treatments that would typically not meet NICEs cost-effectiveness criteria at the current price provides an opportunity to improve access to therapies in the UK, thereby improving public health C_LI

ObjectivesDravet syndrome (DS) is a rare, lifelong epileptic encephalopathy characterised by frequent and severe seizures associated with premature mortality. Typically diagnosed in infancy, patients also experience progressive behavioural, motor-function and cognitive decline. Twenty percent of patients do not reach adulthood. Quality of life (QoL) is impaired for both patients and their carers. Reducing convulsive seizure frequency, increasing seizure free days (SFDs) and improving patient/carer QoL are primary treatment goals in DS. This study explored the relationship between SFDs and patients and carers QoL to inform a cost-utility analysis of fenfluramine. MethodsIn fenfluramine registration studies, patients (or their carer proxies) completed the Paediatric Quality of Life inventory (PedsQOL). These data were mapped to EuroQol-5 Dimensions Youth version (EQ-5D-Y) to provide patient utilities. Carer utilities were collected using EQ-5D-5L and mapped to EQ-5D-3L to align patient and carer QoL on the same scale. Linear mixed-effects and panel regression models were tested and Hausman tests identified the most appropriate approach for each group. On this basis, a linear mixed-effects regression model was used to examine the relationships between patient EQ-5D-Y, and clinically relevant variables (age, frequency of SFDs per 28-days, motor impairments and treatment dose). A linear panel regression model examined the relationship between SFDs and carer QoL. ResultsAdjusting for age and underlying comorbidities, the patient regression model showed that SFDs per 28-days was a significant predictor of QoL. Each additional patient-SFD increased utility by 0.005 (p<.001). The carer linear panel model also showed that increasing SFDs per 28-days was a significant predictor of improved QoL. Each additional SFD increased carer utility by 0.014 (p<.001). SignificanceThis regression framework highlights that SFDs are significantly correlated with both patients and carers QoL. Treatment with effective antiseizure medications that increase SFDs, directly improves QoL for patients and their carers. Short summaryDS patients experience daily severe seizures with progressive deterioration in their physical, cognitive and behavioural development ("comorbidities"), which substantially impacts the QoL of patients and their carers. Reducing seizure frequency and increasing Seizure Free Days (SFDs) are key treatment goals. This study examined the relationship between seizures and patients and carers QoL. Regression analyses were conducted using data from the fenfluramine registration studies and confirmed increasing SFDs directly, and quantifiably, improved patient and carer QoL (utilities per additional SFD per 28-days: patient=0.005 and carer=0.014). These analyses demonstrate that effective antiseizure treatment can directly and profoundly improve patients and carers QoL.

BackgroundAccess to new oncology medicines is subject to delays in many countries due to lengthy appraisal processes. This study examines the cost to the Irish healthcare system of implementing a cost- sharing agreement to expedite access to oncology medicines. MethodsThe hypothetical cost of implementing an early access scheme in Ireland was estimated for oncology medicines commencing appraisal in 2022. The scheme would have required the manufacturer to cover the cost for the first 180-day period and provide a further rebate if costs per patient over the whole duration of access exceeded those that would have arisen from the finally agreed price. Costs of the new medicine and savings arising from any therapies displaced were estimated on a daily basis using data published in the technical summaries of the National Centre for Pharmacoeconomics, Ireland (NCPE) assessment for each medicine. ResultsThe scheme would have reduced the time patients waited to access oncology medicines by more than two years on average. Assuming new medicines attract a discount of 30% at reimbursement and that medicines with an existing agreement are subject to a 30% discount, and that a further discount of 10% would be negotiated at reimbursement for the new indication, the costs to the government over the duration of the scheme would have been {euro}61.9m. Earlier access would have generated an additional 1,621 quality-adjusted life years (QALY) over the lifetime of patients accessing the scheme, after discounting. Costs were sensitive to assumptions on discounts negotiated at reimbursement. Costs fell substantially if patients with private insurance were assumed to access care through that insurance. ConclusionProtracted assessment times lead to substantial health losses to patients with cancer in Ireland. A cost-sharing scheme would accelerate access to new treatments by more than two years and at costs which are unlikely to exceed {euro}62m.

ObjectivesHealth system resources are limited, and decision makers often need to make trade-offs between equity and efficiency. Such trade-offs are guided by public values that are elicited using choice experiments. There is no standard approach to elicit equity-efficiency trade-offs. Previous studies have found significant variability in public values, raising concerns about the robustness of trade-off experiments. The objective of this systematic review was to determine whether and how equity-efficiency trade-off studies consider validity, reliability and framing effects. MethodsWe searched Medline, EMBASE, and Web of Science in January 2024 for health-related equity-efficiency trade-off studies. Two reviewers independently screened titles and abstracts, followed by full-text review, data extraction, and quality assessment. Results115 equity-efficiency trade-off studies were identified, of which 29 studies (25.2%) investigated validity, reliability, and/or framing effects. 14 (12.2%) studies assessed validity, 9 (7.8%) studies assessed reliability, and 10 (8.7%) assessed framing or cognitive effects. Validity was most frequently assessed by comparing results to hypothesized expectations, while reliability was commonly assessed by providing a repeated test or questionnaire. Framing and cognitive effects were assessed by varying question order or changing the wording or framing of the scenario. 19 of 23 studies reported high or acceptable validity or reliability, and 5 of 10 studies found no significant framing or cognitive effects. ConclusionThis systematic review highlights the need to consider robustness of elicited values in choice experiments. It will also guide future choice experiments that aim to estimate inequity aversion.

BackgroundRegular exercise and community engagement may slow the rate of function loss for people with dementia. However, the evidence is uncertain regarding the cost-effectiveness and social return on investment (SROI) of home exercise with community referral for people with dementia. This study aimed to compare the social value generated from the in-person PrAISED programme delivered before March 2020 with a blended PrAISED programme delivered after March 2020. MethodsSROI analysis was conducted alongside a randomised controlled trial (RCT). Of 205 patient participants and their carers who completed cost data, 61 completed an in-person programme before March 2020. Due to COVID-19 pandemic restrictions, 144 patient participants completed a blended programme consisting of a combination of in-person visits, phone calls and video conferencing with multidisciplinary team (MDT) members. SROI analysis compared in-person and blended delivery formats. Five relevant and material outcomes were identified: three outcomes for patient participants (fear of falling, health-related quality of life, and social connection); one outcome for carer participants (carer strain index), and one outcome for the NHS (health service resource use). Data were collected at baseline and a 12-month follow-up. ResultsThe in-person PrAISED programme generated SROI ratios ranging from {pound}0.58 to {pound}2.33 for every {pound}1 invested. In-person PrAISED patient participants gained social value from improved health-related quality of life, social connection, and less fear of falling. In-person PrAISED carer participants acquired social value from less carer strain. The NHS gained benefit from less health care service resource use. However, the blended PrAISED programme generated lower SROI ratios ranging from a negative ratio to {pound}0.08: {pound}1. ConclusionCompared with the blended programme, the PrAISED in-person programme generated higher SROI ratios for people with early dementia. During the COVID-19 pandemic and its restrictions, a blended delivery of the programme and the curtailment of community activities resulted in lower SROI ratios during this period. An in-person PrAISED intervention with community referral is likely to provide better value for money than a blended one with limited community referral, despite the greater costs of the former. Trial registrationISRCTN15320670

BackgroundThe effectiveness of exercise interventions to improve activities of daily living function in people with dementia is inconclusive. This study aimed to assess the long-term cost-effectiveness of the PrAISED intervention from a National Health Service (NHS) perspective. MethodThis novel robust economic analysis used a Markov model to evaluate the incremental cost-effectiveness ratio (ICER) over a lifetime horizon of 15 years for a cohort of patients. Sensitivity analyses were conducted to investigate the uncertainty and robustness of high-impacting parameters and results. ResultsThis study included 365 adults, aged 65 years and above with 183 and 182 randomised to the PrAISED and standard care groups respectively. The PrAISED intervention had mean per-patient cost of {pound}60,465 for the PrAISED arm and {pound}54,604 for standard care. The Praised intervention gained an incremental QALYs of 0.05 resulting in an ICER of {pound}129,614 per QALY. The sensitivity analysis of the intervention cost varied the ICER value between {pound}68,173 and {pound}191,054/QALY. To achieve the recommended NICE willingness to pay threshold value of less than {pound}30,000/QALYs would require the intervention cost to be reduced from {pound}1,236 (current cost) to {pound}263 to break even and be cost-effective. The sensitivity analyses revealed that there was a 40% probability of standard care dominating the PrAISED treatment. ConclusionsAlthough the PrAISED intervention was a low-cost intervention, it did not produce a cost-effective intervention in this analysis. The flexibility of the PrAISED program to adapt to government policy during the COVID-19 pandemic was positive. Trial registrationISRCTN15320670

Abstract Introduction: Daraxonrasib is the first broad RAS-targeted medicine approved for metastatic pancreatic adenocarcinoma. In RASolute 302, it approximately doubled median overall survival in the prespecified RAS G12 population. The US wholesale acquisition cost (WAC) is $39,800 per 30-day supply. Contemporary oncology launch prices have risen substantially, and many recently launched drugs have been priced above value-based benchmarks. The value of a genuinely transformative oncology therapy at this price is uncertain. Objective: To estimate the cost-effectiveness of daraxonrasib compared with investigators-choice chemotherapy in previously treated metastatic pancreatic adenocarcinoma using publicly available clinical and economic data. Design, Setting, and Participants: Economic evaluation using a 3-state partitioned survival model informed by the international randomized phase 3 RASolute 302 trial. Published Kaplan-Meier curves and numbers at risk were used to reconstruct pseudo-individual patient data. The RAS G12 population (n=459), corresponding to the trials dual primary efficacy end points, was the primary economic population; the overall randomized population (n=500) was analyzed in parallel. Analyses were conducted in 2026 from a US health sector perspective. Exposures: Daraxonrasib 300 mg orally once daily versus investigators-choice chemotherapy, weighted to the observed trial regimen distribution. Main Outcomes and Measures: Incremental life-years, quality-adjusted life-years (QALYs), costs, incremental cost-effectiveness ratio (ICER), incremental net monetary benefit, and the 30-day daraxonrasib price consistent with willingness-to-pay thresholds of $100,000, $150,000, $200,000, and $255,000/QALY. Costs were expressed in 2026 US dollars and discounted at 3% annually. Results: In the RAS G12 population, daraxonrasib generated 0.652 additional life-years and 0.506 additional QALYs at an incremental cost of $253,171, yielding an ICER of $500,637/QALY. The corresponding overall-population ICER was $542,255/QALY. A 10-year horizon changed the RAS G12 ICER to $497,270/QALY; using curve-specific distributions selected by Akaike information criterion produced $493,895/QALY. In 5000 probabilistic simulations, the probability of cost-effectiveness was 0% at $150,000 and $200,000/QALY and 0.1% at $255,000/QALY. Threshold prices in the RAS G12 population were $14,060 per 30 days at $150,000/QALY and $17,730 at $200,000/QALY, reductions of 64.7% and 55.5% from WAC. An independent secondary implementation using separately digitized survival curves and piecewise-exponential extrapolation produced a higher ICER; after calibrating drug exposure to observed treatment duration, the estimate remained approximately $676,000/QALY, with most of the difference attributable to the extrapolated treatment-exposure tail. Conclusions and Relevance: Daraxonrasib nearly doubled median overall survival in previously treated metastatic pancreatic adenocarcinoma, in which median survival with standard chemotherapy was about 7 months, and should remain available to eligible patients. At WAC, daraxonrasib costs approximately $500,000 per QALY, well above conventional US benchmarks. Because actual net acquisition prices may reflect confidential discounts or rebates, the ICER based on WAC may exceed the ICER at the net price. Threshold list prices consistent with $150,000-$200,000/QALY were $14,060-$17,730 per 30 days (a 55%-65% reduction from WAC), offered as reference points for pricing and coverage discussions rather than recommended prices.

BACKGROUNDAmong patients with type 2 diabetes and a history of strokes or transient ischemic attacks, pioglitazone significantly reduces the risk of recurrent stroke. The Insulin Resistance Intervention in Stroke (IRIS) trial found that pioglitazone also reduced the risks of stroke or transient ischemic attacks and new-onset diabetes among patients with insulin resistance. As reported by our previous work, the low-dose pioglitazone was found to provide most of the clinical benefit of high-dose pioglitazone, with fewer adverse effects. We report a model-based economic evaluation to determine the cost-effectiveness of the low-dose pioglitazone versus placebo. METHODSA lifetime Markov model, with an annual cycle length and five health states (event-free, myocardial infarction, stroke, new-onset diabetes, death), was developed. Transition probabilities were extracted from a subgroup of IRIS patients with insulin resistance, defined by a glycosylated hemoglobin level of 5.7% to 6.4% (mean follow-up of 5 years). Health state costs and utilities were based on public sources and literature data, respectively. Utilities were weighted by time spent in health states to calculate quality-adjusted life years (QALYs). The incremental cost per QALY gained was estimated for the population. Annual discount rates of 0%, 1.5%, and 3% were applied. In addition to deterministic analyses, probabilistic sensitivity analyses were conducted to deal with parameter uncertainty. The analyses were conducted from a Canadian public payer perspective in 2023 Canadian dollars. RESULTSThe base case results indicated that over a lifetime, the expected costs were CAN$31,534 for low-dose pioglitazone and CAN$55,076 for placebo, resulting in a cost saving of CAN$23,542 in favor of the low-dose pioglitazone. Expected QALYs were 25.10 for the low-dose pioglitazone daily and 19.32 for placebo, resulting in a difference of 5.78 QALYs in favor of low-dose pioglitazone. Probability sensitivity analyses with varying discount rates confirmed these results. CONCLUSIONSCompared with placebo, low-dose pioglitazone is the dominant strategy. Key PointsO_ST_ABSQuestionC_ST_ABSCompared with a placebo, is low-dose pioglitazone cost-effective in treating stroke/ transient ischemic attack (TIA) and new-onset diabetes in a simulated population with prediabetics? FindingsOver a lifetime, the expected costs were CAN$31,534 for low-dose pioglitazone and CAN$55,076 for placebo, resulting in a cost saving of CAN$23,542 in favor of low-dose pioglitazone. Expected QALYs were 25.10 for low-dose pioglitazone daily and 19.32 for placebo, resulting in a difference of 5.78 QALYs in favor of low-dose pioglitazone. Sensitivity and scenario analyses confirmed the results. MeaningThe model-based economic evaluation indicates that low-dose pioglitazone, compared with placebo, is the dominant strategy.

Background. Comprehensive genomic reports in oncology contain complex molecular information that must be translated into concise summaries for treating clinicians. Manual summarisation is time-intensive, and robust evidence supporting AI-assisted summarisation systems remains limited. We evaluated whether AI-generated summaries were non-inferior to manually produced summaries in faithfully representing information from tertiary paediatric cancer genomics reports. Methods. QNOMX-VHIR-CPSP-001 Phase 1 was a single-site, non-interventional clinical performance study conducted at Vall d'Hebron Institut de Recerca under ISO 20916:2019. De-identified tertiary paediatric cancer genomics reports were summarised using both the AI-assisted system under evaluation and the standard manual workflow. Three qualified raters assessed both summary types against the source reports using the six-domain, five-point Quality Summary Index (QSI) in a blinded, counterbalanced crossover design with a minimum fourteen-day washout. Co-primary endpoints were composite content and presentation scores. Non-inferiority was assessed using a pre-specified Bayesian hierarchical model with a margin of 0.5 points and confirmed using a frequentist linear mixed model. Results. Thirty-seven of 38 planned cases contributed 74 paired evaluations. Mean content scores were similar between AI-generated and manual summaries (4.01 vs 3.97), whereas AI-generated summaries achieved higher presentation scores (4.39 vs 4.12). Non-inferiority was demonstrated for both co-primary endpoints: content +0.05 (95% CrI -0.18 to +0.28), presentation +0.28 (+0.10 to +0.47). Frequentist analyses were concordant. Accuracy was the only QSI dimension for which Bayesian non-inferiority was not concluded. Inter-rater reliability was low across all QSI domains. Conclusions. AI-generated summaries were non-inferior to manually produced summaries for both content and presentation endpoints in tertiary paediatric oncology genomics reports. Presentation scores were higher for AI-generated summaries, whereas content scores were comparable between approaches. Low inter-rater reliability limits confidence in the dimension-level estimates and is the main constraint on future validation studies. Clinical validity and workflow effect were not assessed.

ObjectivesTo develop within-patient meaningful score differences (MSDs) on the Bayley Scales of Infant Development, Fourth Edition (Bayley-4), and the Vineland Adaptive Behavior Scales, Third Edition (Vineland-3), for individuals with Angelman syndrome (AS). MethodsA Delphi method, involving a panel of 19 caregivers of individuals with AS, was used to establish MSDs for Bayley-4 and Vineland-3 Growth Scale Values. MSD was defined as the smallest change that would noticeably impact the daily functioning of an individual with AS or family quality of life in a way that was important to the individual with AS or their family. For each subscale of the Bayley-4 and Vineland-3, the panel was presented with 2 to 4 vignettes describing varying levels of baseline functioning and asked to select a MSD from a range of potential values. An iterative process involving three rounds of ratings and two rounds of discussion was used to build consensus. The median caregiver rating from round 3 was retained as the final recommended MSD value for each vignette. ResultsFinal MSD ratings for the five subscales of Bayley-4 and 10 subscales of the Vineland-3 had an agreement rate of 70% or higher. MSD thresholds for each subscale were not single cut-offs, but rather reflected a range of MSD values dependent on level of baseline functioning. ConclusionsThe Delphi Panel method incorporates the caregiver perspective to provide preliminary estimates of what constitutes meaningful within person change on the Bayley-4 and Vineland-3 in individuals with AS with various levels of baseline functioning. Highlights To acquire regulatory approval in drug development, sponsors must demonstrate both statistical significance and clinical meaningfulness of a treatment effect.While several clinical trials are underway in AS, within person meaningful score difference thresholds are not yet established for the most commonly used outcome measures, namely the Bayley and Vineland. Aligning with FDA guidance, we have developed an innovative qualitative approach using a Delphi panel to incorporate caregiver perspectives in defining meaningful change and generated preliminary patient-informed meaningful score differences (MSDs) for individuals with Angelman Syndrome. What caregivers of individuals with AS consider to be a MSD on the measures depends primarily on the baseline severity of their childs presentation.

Background: The impact of the Inflation Reduction Act (IRA) upon late-stage developments has been assumed to be limited. The Congressional Budget Office's IRA analysis excluded post-approval innovation, potentially overlooking substantial economic risks to drug developers and declines in the availability of treatments in areas of high unmet medical need such as oncology. Methods: A total of 1148 secondary trials from 364 FDA-approved medicines, published from 2018 to 2025, were obtained from Biomedtracker and clinicaltrials.gov. Using fractional multinomial logit, we model the share distribution of secondary indication studies across 19 disease groups and assess the change in this distribution post-IRA. We also assessed the number of secondary treatment studies pre- vs. post-IRA using multiple linear regression. Results: After the IRA's introduction, small molecule follow-on studies in oncology exhibited a statistically significant 35% decline (R2 = .48, p < 0.014) and lead indication small molecule oncology approvals exhibited a statistically significant 27% decline (R2 = .70, p < 0.002). We also find a statistically significant 14% decline in the share of orphan oncology studies pre- vs. post-IRA (p<0.001). Research Conclusions: This study's results refute claims that the IRA would have minimal negative effects on patient access or late-stage biopharmaceutical R&D. We hope this study reinvigorates debate about the law's unintended consequences and encourages thoughtful policy solutions, as the IRA manifestly creates disincentives that negatively impact patients seeking needed new medicines, particularly those requiring cures addressing metastatic late-stage cancers.

BackgroundIn the context of high-risk surgery, shared decision-making (SDM) can be hindered by misalignment in expectations regarding the likely surgical outcomes between patients and clinicians. This study investigates the extent of this misalignment in high-risk patients and doctors involved in perioperative care, its impact on treatment choices, and its amenability to interventions that encourage perspective taking. MethodsHigh-risk patients (N = 55) and doctors involved in perioperative care (N = 54) were asked to consider one of three clinical scenarios: ischaemic heart disease, colorectal cancer, or osteoarthritis of the left hip. They reported on their expectations regarding short- and long-term outcomes of different treatment options available in these scenarios. Participants were initially asked to consider the scenarios from their own perspective as a patient/clinician, and then to adopt the perspective of the other side. After stating their expectations, participants were required to choose between surgical or non-surgical treatment. ResultsSystematic misalignment in expectations between high-risk patients and doctors was observed, with patients expecting better surgical outcomes compared to clinicians. Despite this misalignment, in both groups surgical treatment was strongly preferred. Willingness to consider the non-surgical option was only observed when this option offered a chance to change the undesirable status quo. ConclusionWhen high-risk surgery is discussed, a non-surgical option may be viewed as doing nothing, reducing the sense of agency and control. This biases the decision-making process, regardless of the expectations doctors and patients might have about the outcomes of surgery. Thus, to improve SDM and to increase patients agency and control over decisions about their care, we advocate framing the non-surgical treatment options in a way that emphasises action, agency, and change. HighlightsO_LIMisalignment in expectations regarding treatment outcomes between high-risk surgical patients and their clinicians has been identified in this study, with patients expecting more positive outcomes from surgery than doctors C_LIO_LIDespite misalignment, treatment choices were similar for patients and clinicians C_LIO_LIFraming the treatment choice as doing something (i.e. surgery) vs. doing nothing seemed to drive the preference for surgery in both groups C_LIO_LITo increase patients agency and control over decisions about surgery, the framing of their options should be targeted for improvement C_LI

1.2.IntroductionAI interventions for health care are on the rise. Decisions about coverage and reimbursement are often informed by Health Technology Assessment (HTA) bodies, who rely on Health Economic Evaluations (HEEs) to estimate the value for money (cost effectiveness) of interventions. Transparent reporting of HEEs ensures they can be used for decision making. Reporting guidance exists to support this, such as the Consolidated Health Economic Reporting Standards (CHEERS) checklist. We aim to identify consensus about specific items should be reported by HEEs that evaluate AI interventions and, if such items are identified, to develop them into an extension to CHEERS: "CHEERS-AI". Methods and analysisThe project will have 4 phases: O_LIPhase 1 is a literature review to help identify potential AI-related reporting items. C_LIO_LIPhase 2 commences a Delphi process, with a series of surveys to elicit the importance of the potential AI-related reporting items. C_LIO_LIPhase 3 is a consensus-generation meeting to agree on the final extension items. C_LIO_LIPhase 4 is dissemination of the projects outputs. C_LI Ethics and disseminationThis study has received ethical approval from Newcastle University Ethics Committee (reference: 28568/2022). The findings will be available in as an open access article and disseminated through blogs, newsletters, and presentations. 1.3. Funding statementThis study is supported by the Next Generation Health Technology Assessment (HTx) project. The HTx project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement N{o} 825162. This dissemination reflects only the views of the authors and the Commission is not responsible for any use that may be made of the information it contains.