Health Economic Model Research Articles

A Systematic Review of Modeling Approaches to Evaluate Treatments for Relapsed Refractory Multiple Myeloma: Critical Review and Considerations for Future Health Economic Models.

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. 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. 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. 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.

P47 GPT: The Next Frontier in Health Economic Modeling?

P47 GPT: The Next Frontier in Health Economic Modeling?

SA6 Balancing Feasibility, Time, and Comprehensiveness: Approaches to Rapid Reviews of Health Economic Models

SA6 Balancing Feasibility, Time, and Comprehensiveness: Approaches to Rapid Reviews of Health Economic Models

SA3 Quality of Systematic Literature Reviews of Health Economic Models

SA3 Quality of Systematic Literature Reviews of Health Economic Models

EE202 Conceptualization and Validation of a Health Economic Model for Osteogenesis Imperfecta

EE202 Conceptualization and Validation of a Health Economic Model for Osteogenesis Imperfecta

AI-based approach to enable proactive identification of early lung cancer: A retrospective population health study and economic model.

10536 Background: Approximately 48,500 new cases of lung cancer are diagnosed each year in the UK with a five-year survival rate which lags behind other European countries. Most patients are diagnosed at stage III or IV disease, which has a significantly worse prognosis. The integration of real-world data and AI techniques presents a promising avenue for improvement, using novel solutions not achievable through traditional care models. This study explores proactive patient identification at scale, aligning with the "left shift" paradigm in population health. The approach uses innovative technologies and novel data sources to identify patients with early-stage disease, enhancing both clinical and economic outcomes. Importantly, these interventions should be feasible on a large scale without overburdening already stretched clinical services. Methods: A retrospective study of real-world data for an urban population with high socioeconomic disadvantage and health needs was undertaken. Two endpoints were determined: detection potential and health economic impact. For detection potential (DP), patients aged 40 and above who underwent chest X-rays (CXR) at a large academic medical centre between 2016-2022 were included. Natural Language Processing (NLP) analysed unstructured free-text data from Electronic Health Records, combined with ICD-10 codes, for predictive model (PM) development using Machine Learning (ML) classification techniques. The PM's performance was assessed using the area under the receiver operating characteristic curve (AUCROC). Health economic impact was evaluated through a theoretical extrapolation of DP. Assuming a 9% left shift from late (stage III or IV) to early (stage I or II) disease, a health economic model (HEM) compared the impact of the current care model to a potential future state facilitated by the digital medicine care model (DMCM). Results: 75,342 patients were included, with 755 lung cancer diagnoses occurring within 6 months of CXR. The PM achieved a peak AUROC of 0.75, exceeding the performance of currently used risk prediction models (Q Cancer Risk, Risk Assessment Tools). A 9% left shift equated to 4.5 years mean increase in survival. The DMCM was associated with a net healthcare cost benefit of £6k per patient diagnosed with lung cancer, improved quality-adjusted life years of £21M for the year 1 cohort over 10 years and increased economic productivity (from additional years in workforce) of £2.4M. Conclusions: The study demonstrates the practical utility of AI-supported early lung cancer detection at population scale. Real-world evidence is needed to prospectively validate the model as cost-effective, scalable, and efficient. Future work will seek to improve the model's performance against a broader at-risk patient population and explore its applicability to other late-presenting tumours.

Comparison of zanubrutinib (zanu) and acalabrutinib (acala) in B-cell malignancies: An adverse event (AE)-based analysis.

e19049 Background: Acala and zanu are 2nd generation Bruton tyrosine kinase inhibitors (BTKis) that have demonstrated improved safety profiles vs 1st generation BTKi ibrutinib in clinical trials. While head-to-head comparison of zanu vs acala is lacking, a recent meta-analysis of clinical trials provided a comprehensive, indirect comparison of the AE profiles in B-cell malignancies (Hwang, Hemasphere. 2023;7(S3):1134). This study aims to evaluate costs and impacts on quality of life (QoL) for zanu vs acala using their AE profiles. Methods: A health economic model was developed to determine cost and QoL associated with AE management for zanu and acala. The incidence rates ( IRs) of all grade and grade ≥3 AEs of interest (n=21, including bleeding events, hypertension, atrial fibrillation, cytopenias, infections, headache, arthralgia, diarrhea) were taken from the meta-analysis. Additional model inputs, such as disutility, average duration of AE, and unit cost of each AE were sourced from published literature and publicly available cost databases in consultation with clinical experts. The model was developed from the US perspective, considering Medicare costs, inflated to 2023 USD. Model outcomes were AE management cost (= IR * unit cost) and quality-adjusted life years (QALYs) lost due to AE (= IR * Disutility * Duration/365.25). Results: In the base-case scenario (considering all AEs), treating a hypothetical cohort of 1000 patients (pts) with zanu instead of acala yielded cost savings of $124K and 3.7 QALY gains (i.e., 3.7 years extra in full health). Subgroup analysis for severe (grade ≥3) and non-severe AEs (grade 1-2) demonstrated similar trends. A sensitivity analysis limited to AEs significantly different between zanu and acala (n=13) yielded consistent results. Additionally, a probabilistic sensitivity analysis, conducted with 1000 iterations to account for uncertainty in model parameters, confirmed robustness of the model, indicating stable conclusions across a wide range of parameter uncertainties. Conclusions: This analysis suggested that zanu is cost-saving and associated with added health benefits compared to acala in terms of AE management in pts with B-cell malignancies. Should these results derived from meta-analysis of clinical trial data be applicable to real-world pt populations across different indications, the potential cost savings could be considerable. These results could be further improved if the differences in efficacy of zanu vs acala are also incorporated. [Table: see text]

Clinical and economic benefit of genomic testing strategies to guide the treatment of patients with HR+/HER2- breast cancer in the US.

10584 Background: Multiple testing technologies are available to inform treatment decisions for hormone receptor positive (HR+) and human epidermal growth factor receptor 2 negative (HER2-) early-stage breast cancer, including tumor profiling and germline genetic testing (GGT). The 21-gene assay provides an estimate of distant recurrence risk and potential clinical benefit of chemotherapy. GGT identifies pathogenic variants which inform lifetime cancer risk as well may inform therapy options. This study explored the potential synergy of utilizing both tests by assessing the cost-effectiveness of this strategy compared to no testing from a societal perspective. Methods: A health economic model was developed using best practices to assess a patient cohort with HR+/HER2-, node-negative (N0) or node-positive (N1) breast cancer that was guided to surgical and adjuvant treatment by GGT ( ATM, BRCA1/2, CDH1, CHEK2, PALB2, PTEN,and TP53) and the 21-gene assay. Treatment effectiveness was informed by clinical trials (TAILORx and RxPONDER); costs for treatment, monitoring, and health utilities were sourced from literature. Clinical expert opinion and guidelines informed current US clinical practice. The base case analysis evaluated lifetime total costs, life-years, and quality-adjusted life years (QALYs) versus no testing. Scenario and sensitivity analyses were performed to evaluate model robustness. Results: The testing strategy was dominant (lower cost and higher QALYs gained) compared to no testing leading to a QALY improvement of 0.286 and cost savings of $16,463 in the N0 population (Table) and a 0.099 QALY improvement and $299 cost savings in the N1 population. Scenario analyses indicated that while the 21-gene assay was the primary driver of results, there were synergistic effects when tests were combined. Sensitivity analysis supported cost-effectiveness at most willingness-to-pay thresholds and identified the probability of chemotherapy and rate of distant recurrence as the most impactful model inputs. Conclusions: Using a testing strategy including both the 21-gene assay and GGT enables optimization of treatment decisions in early HR+/HER2- breast cancer, improving patient outcomes at reduced costs. [Table: see text]

EE111 Optimization of the Health Economic Model of Type 2 Diabetes Based on Glycemic Variability Mechanism

EE111 Optimization of the Health Economic Model of Type 2 Diabetes Based on Glycemic Variability Mechanism

A Model-Based Economic Evaluation of Hypothetical Treatments for Amyotrophic Lateral Sclerosis in the UK: Implications for Pricing of New and Emerging Health Technologies.

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. 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. 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. 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. 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.

Utilities for Complications Associated with Type 2 Diabetes: A Review of the Literature.

Utility values are used in health economic modeling analyses of type 2 diabetes (T2D) to quantify the effect of acute and long-term complications on quality of life (QoL). For accurate modeling projections, it is important that the utility values used are up to date, accurate and representative of the simulated model cohort. A literature review was performed to identify utility values for health states representing acute and chronic T2D-related complications including cardiovascular complications, stroke, renal disease, ophthalmic complications, neuropathy, diabetic foot, amputation and hypoglycemia. Searches were performed using the PubMed, Embase and Cochrane Library databases and limited to articles published since 2010. Supplementary searches were performed to identify data published at congresses in 2019-2023. A total of 54 articles were identified that reported utility values for T2D-related complications. The most frequently used elicitation method/instrument was the EQ-5D (n = 42 studies) followed by the Short Form-6 dimensions (n = 6), time tradeoff (n = 5), the Health Utilities Index Mark 2 or Mark 3 (n = 2), 15D (n = 1), visual analog scale (n = 1) and standard gamble (n = 1). Stroke and amputation were consistently associated with the largest decrements in QoL. There is a lack of published data that distinguishes between severity of several complications including renal disease, retinopathy and neuropathy. Diabetes-related complications can have a profound impact on QoL; therefore, it is important that these are captured accurately and appropriately in health economic models. Recently published utility values for diabetes-related complications that can be used to inform health economic models are summarized here.

A Prototype Software Framework for Transferable Computational Health Economic Models and Its Early Application in Youth Mental Health.

We are developing an economic model to explore multiple topics in Australian youth mental health policy. To help make that model more readily transferable to other jurisdictions, we developed a software framework for authoring modular computational health economic models (CHEMs) (the software files that implement health economic models). We specified framework user requirements for: a simple programming syntax; a template CHEM module; tools for authoring new CHEM modules; search tools for finding existing CHEM modules; tools for supplying CHEM modules with data; reproducible analysis and reporting tools; and tools to help maintain a CHEM project website. We implemented the framework as six development version code libraries in the programming language R that integrate with online services for software development and research data archiving. We used the framework to author five development version R libraries of CHEM modules focussed on utility mapping in youth mental health. These modules provide tools for variable validation, dataset description, multi-attribute instrument scoring, construction of mapping models, reporting of mapping studies and making out of sample predictions. We assessed these CHEM module libraries as mostly meeting transparency, reusability and updatability criteria that we have previously developed, but requiring more detailed documentation and unit testing of individual modules. Our software framework has potential value as a prototype for future tools to support the development of transferable CHEMs.Code: Visit https://www.ready4-dev.com for more information about how to find, install and apply the prototype software framework.

Epidemiological and health economic implications of symptom propagation in respiratory pathogens: A mathematical modelling investigation.

Respiratory pathogens inflict a substantial burden on public health and the economy. Although the severity of symptoms caused by these pathogens can vary from asymptomatic to fatal, the factors that determine symptom severity are not fully understood. Correlations in symptoms between infector-infectee pairs, for which evidence is accumulating, can generate large-scale clusters of severe infections that could be devastating to those most at risk, whilst also conceivably leading to chains of mild or asymptomatic infections that generate widespread immunity with minimal cost to public health. Although this effect could be harnessed to amplify the impact of interventions that reduce symptom severity, the mechanistic representation of symptom propagation within mathematical and health economic modelling of respiratory diseases is understudied. We propose a novel framework for incorporating different levels of symptom propagation into models of infectious disease transmission via a single parameter, α. Varying α tunes the model from having no symptom propagation (α = 0, as typically assumed) to one where symptoms always propagate (α = 1). For parameters corresponding to three respiratory pathogens-seasonal influenza, pandemic influenza and SARS-CoV-2-we explored how symptom propagation impacted the relative epidemiological and health-economic performance of three interventions, conceptualised as vaccines with different actions: symptom-attenuating (labelled SA), infection-blocking (IB) and infection-blocking admitting only mild breakthrough infections (IB_MB). In the absence of interventions, with fixed underlying epidemiological parameters, stronger symptom propagation increased the proportion of cases that were severe. For SA and IB_MB, interventions were more effective at reducing prevalence (all infections and severe cases) for higher strengths of symptom propagation. For IB, symptom propagation had no impact on effectiveness, and for seasonal influenza this intervention type was more effective than SA at reducing severe infections for all strengths of symptom propagation. For pandemic influenza and SARS-CoV-2, at low intervention uptake, SA was more effective than IB for all levels of symptom propagation; for high uptake, SA only became more effective under strong symptom propagation. Health economic assessments found that, for SA-type interventions, the amount one could spend on control whilst maintaining a cost-effective intervention (termed threshold unit intervention cost) was very sensitive to the strength of symptom propagation. Overall, the preferred intervention type depended on the combination of the strength of symptom propagation and uptake. Given the importance of determining robust public health responses, we highlight the need to gather further data on symptom propagation, with our modelling framework acting as a template for future analysis.

Abstract PO5-19-01: The HER2-RADiCAL study (Response ADaptive CAre pLan) – Tailoring treatment for HER2 positive early breast cancer

Abstract Background The presence or absence of residual disease following neoadjuvant systemic anti-cancer therapy (neoSACT) for HER2-positive early breast cancer (HER2+ EBC) provides powerful prognostic information. Pathological complete response (pCR) following neoSACT, particularly in patients with earlier clinical TNM stage at diagnosis, identifies a population with excellent survival outcomes in whom the balance of toxicity associated with the current treatment pathway may be disproportionate to absolute clinical benefit. Aims >HER2-RADiCAL seeks to reduce the treatment burden and healthcare costs of treating HER2+ EBC by testing the hypothesis that pCR can be used as a functional response biomarker to select patients who can safely receive less systemic therapy with minimal/no loss of efficacy. Trial design and eligibility criteria HER2-RADiCAL is a response-directed interventional cohort/threshold-crossing design study embedded within a real-world data-driven clinical pathway model. Patients with ER-positive or negative HER2+ EBC with cT1N1 or cT2N0-1 stage at diagnosis are eligible after completion of standard of care neoSACT and locally determined pCR (ypT0/Tis ypN0). Patients with pathological features consistent with previous malignant involvement in >4 nodes are not eligible. Adjuvant trastuzumab +/- pertuzumab may have continued prior to study entry provided no more than 9 cycles of trastuzumab are received. After registration all participants receive a total of 9 cycles of trastuzumab including those administered prior to study entry. Participants receive no further pertuzumab and no adjuvant chemotherapy. Adjuvant endocrine therapy and radiotherapy are given as per standard of care. Central pathology review of pCR status will be conducted in a subset of cases. A Study Within a Trial (SWAT) will explore identification of factors influencing patient decision-making for participation in studies of response-adapted treatment. Statistical methods The primary endpoint is relapse-free interval. Recruitment of 720 participants over 3.5 years will provide 90% power to exclude an event rate >6.5% at 3 years (expected event rate ≤4%). Secondary endpoints include relapse-free survival, invasive breast cancer-free survival, invasive disease-free survival, distant recurrence-free interval, breast cancer-free interval, overall survival, treatment pathway adherence and cost-effectiveness. An interventional cohort is preferred to a randomised non-inferiority trial based on the known event rate in this population which is sufficiently low such that any deviation from this expected event rate would indicate failure of the reduced therapy strategy. Real-world data driven clinical pathway model Health economic modelling will compare the protocol-driven study cohort with two comparator pathways: a non-response adapted maximum therapy pathway (the standard clinical pathway prior to the study) and a real-world representative pathway derived from anonymised data for all patients treated for HER2+ EBC within the UK NHS. Patient and public involvement Our PPI partners continue to shape this research, holding key roles as trial management group members, overseeing progress of the study, contributing to protocol amendment development, and design and implementation of the SWAT. Current status The first patient was enrolled in December 2021. At 10th July 2023, 25 research sites have opened with 39 participants recruited. Site opening continues with a minimum target of 40. Responding to continued use of anthracycline-based regimens in UK practice, eligibility criteria have recently been extended to permit entry of patients who have received anthracycline-containing neoSACT. For further information contact her2radical-icrctsu@icr.ac.uk. Citation Format: Iain Macpherson, Stuart McIntosh, Lucy Kilburn, Holly Tovey, Laura Moretti, Katie Goddard, Indrani Bhattacharya, Clinton Boyd, Cliona Kirwan, Ciara O'Brien, Duncan Wheatley, Jennifer Glendenning, Mairead MacKenzie, Hilary Stobart, Claire Snowdon, Andrew Wardley, Abeer Shaaban, Peter Hall, David Cameron, Judith Bliss. The HER2-RADiCAL study (Response ADaptive CAre pLan) – Tailoring treatment for HER2 positive early breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-19-01.

Estimating the Lifetime Costs and Benefits of the Incredible Years Teacher Classroom Management Intervention Using Data From 30 Months Follow-Up of the Supporting Teachers and childRen in Schools Trial

ObjectivesThe Incredible Years Teacher® Classroom Management (IY-TCM) intervention is associated with short-term improvements in mental health difficulties in young people. The aim was to estimate the long-term impact and cost-effectiveness of the IY-TCM intervention compared with no intervention. MethodsAn existing health economic model (LifeSim 1.0) was used to translate short-term changes in the Strength and Difficulties Questionnaire (SDQ), based on the Supporting Teachers and childRen in Schools cluster randomized controlled trial of the IY-TCM intervention in schools, into estimated medium- and long-term effects using multiple longitudinal data sets. LifeSim 1.0 was adapted to incorporate teacher-reported SDQ and account for individual heterogeneity. Cost-effectiveness analyses were conducted using the trial-based intervention cost with subgroup analyses on deprivation, conduct scores and parental depression in the simulated baseline population. ResultsRegression analyses show significant predictor variables for intervention effectiveness, including deprivation and baseline SDQ. LifeSim results indicate small gains in long-term outcomes, and cost-effective analyses estimated that the IY-TCM intervention could be cost-effective, but there was a large amount of uncertainty (net monetary benefit = £10, Estimated CI = −£134, £156). Benefits and certainty of cost-effectiveness were greater for some subgroups, such as those with high conduct scores at baseline (net monetary benefit = £206, Estimated CI = £26, £318). ConclusionsIY-TCM could be cost-effective, but there was a large amount of uncertainty around costs and benefits. Greater benefits for pupils with difficulties at baseline suggest that the intervention may be more cost-effective for schools in more deprived areas with high levels of conduct problems.

Cost-effectiveness of incentives for physical activity in coronary heart disease in Germany: pre-trial health economic model of a complex intervention following the new MRC framework

ObjectivesThe German Incentives for Physical Activity in Cardiac Patients trial is a three-arm, randomised controlled trial for secondary prevention of coronary heart disease (CHD). Guidance for developing complex interventions recommends...

Cost-effectiveness of 4CMenB vaccination against gonorrhea: importance of dosing schedule, vaccine sentiment, targeting strategy, and duration of protection.

Observational evidence suggests the 4CMenB meningococcal vaccine may partially protect against gonorrhea, with one dose being two-thirds as protective as two. We examined the cost-effectiveness of vaccinating men-who-have-sex-with-men (MSM) in England, with one- or two-dose primary vaccination. Integrated transmission-dynamic health-economic modeling explored the effects of targeting strategy, first- and second-dose uptake levels, and duration of vaccine protection, using observational estimates of vaccine protection. Vaccination with one or two primary doses is always cost-saving, irrespective of uptake, although vaccine sentiment is an important determinant of impact and cost-effectiveness. The most impactful and cost-effective targeting is offering "Vaccination-according-to-Risk" (VaR), to all patients with gonorrhea plus those reporting high numbers of sexual partners. If VaR is not feasible to implement then the more-restrictive strategy of "Vaccination-on-Diagnosis" (VoD) with gonorrhea is cost-effective, but much less impactful. Under conservative assumptions, VaR(2-dose) saves £7.62M(95%CrI:1.15-17.52) and gains 81.41(28.67-164.23) QALYs over 10 years; VoD(2-dose) saves £3.40M(0.48-7.71) and gains 41.26(17.52-78.25) QALYs versus no vaccination. Optimistic versus pessimistic vaccine-sentiment assumptions increase net benefits by ∼30%(VoD) or ∼60%(VaR). At UK costs, targeted 4CMenB vaccination of MSM gains QALYs and is cost-saving at any uptake level. Promoting uptake maximizes benefits and is an important role for behavioral science.

A Blueprint for Multi-use Disease Modeling in Health Economics: Results from Two Expert-Panel Consultations.

The current use of health economic decision models in HTA is mostly confined to single use cases, which may be inefficient and result in little consistency over different treatment comparisons, and consequently inconsistent health policy decisions, for the same disorder. Multi-use disease models (MUDMs) (other terms: generic models, whole disease models, disease models) may offer a solution. However, much is uncertain about their definition and application. The current research aimed to develop a blueprint for the application of MUDMs. We elicited expert opinion using a two-round modified Delphi process. The panel consisted of experts and stakeholders in health economic modelling from various professional backgrounds. The first questionnaire concerned definition, terminology, potential applications, issues and recommendations for MUDMs and was based on an exploratory scoping review. In the second round, the panel members were asked to reconsider their input, based on feedback regarding first-round results, and to score issues and recommendations for priority. Finally, adding input from external advisors and policy makers in a structured way, an overview of issues and challenges was developed during two team consensus meetings. In total, 54 respondents contributed to the panel results. The term 'multi-use disease models' was proposed and agreed upon, and a definition was provided. The panel prioritized 10 potential applications (with comparing alternative policies and supporting resource allocation decisions as the top 2), while 20 issues (with model transparency and stakeholders' roles as the top 2) were identified as challenges. Opinions on potential features concerning operationalization of multi-use models were given, with 11 of these subsequently receiving high priority scores (regular updates and revalidation after updates were the top 2). MUDMs would improve on current decision support regarding cost-effectiveness information. Given feasibility challenges, this would be most relevant for diseases with multiple treatments, large burden of disease and requiring more complex models. The current overview offers policy makers a starting point to organize the development, use, and maintenance of MUDMs and to support choices concerningwhich diseases and policy decisions they will be helpful for.

Value of Information for Clinical Trial Design: The Importance of Considering All Relevant Comparators.

Value of Information (VOI) analyses calculate the economic value that could be generated by obtaining further information to reduce uncertainty in a health economic decision model. VOI has been suggested as a tool for research prioritisation and trial design as it can highlight economically valuable avenues for future research. Recent methodological advances have made it increasingly feasible to use VOI in practice for research; however, there are critical differences between the VOI approach and the standard methods used to design research studies such as clinical trials. We aimed to highlight key differences between the research design approach based on VOI and standard clinical trial design methods, in particular the importance of considering the full decision context. We present two hypothetical examples to demonstrate that VOI methods are only accurate when (1) all feasible comparators are included in the decision model when designing research, and (2) all comparators are retained in the decision model once the data have been collected and a final treatment recommendation is made. Omitting comparators from either the design or analysis phase of research when using VOI methods can lead to incorrect trial designs and/or treatment recommendations. Overall, we conclude that incorrectly specifying the health economic model by ignoring potential comparators can lead to misleading VOI results and potentially waste scarce research resources.

The value of artificial intelligence for the treatment of mechanically ventilated intensive care unit patients: An early health technology assessment

PurposeThe health and economic consequences of artificial intelligence (AI) systems for mechanically ventilated intensive care unit patients often remain unstudied. Early health technology assessments (HTA) can examine the potential impact of AI systems by using available data and simulations. Therefore, we developed a generic health-economic model suitable for early HTA of AI systems for mechanically ventilated patients. Materials and methodsOur generic health-economic model simulates mechanically ventilated patients from their hospitalisation until their death. The model simulates two scenarios, care as usual and care with the AI system, and compares these scenarios to estimate their cost-effectiveness. ResultsThe generic health-economic model we developed is suitable for estimating the cost-effectiveness of various AI systems. By varying input parameters and assumptions, the model can examine the cost-effectiveness of AI systems across a wide range of different clinical settings. ConclusionsUsing the proposed generic health-economic model, investors and innovators can easily assess whether implementing a certain AI system is likely to be cost-effective before an exact clinical impact is determined. The results of the early HTA can aid investors and innovators in deployment of AI systems by supporting development decisions, informing value-based pricing, clinical trial design, and selection of target patient groups.