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

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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 concerning which diseases and policy decisions they will be helpful for.

Software with artificial intelligence-derived algorithms for analysing CT brain scans in people with a suspected acute stroke: a systematic review and cost-effectiveness analysis.

Background: Artificial intelligence-derived software technologies have been developed that are intended to facilitate the review of computed tomography brain scans in patients with suspected stroke. Objectives: To evaluate the clinical and cost-effectiveness of using artificial intelligence-derived software to support review of computed tomography brain scans in acute stroke in the National Health Service setting. Methods: Twenty-five databases were searched to July 2021. The review process included measures to minimise error and bias. Results were summarised by research question, artificial intelligence-derived software technology and study type. The health economic analysis focused on the addition of artificial intelligence-derived software-assisted review of computed tomography angiography brain scans for guiding mechanical thrombectomy treatment decisions for people with an ischaemic stroke. The de novo model (developed in R Shiny, R Foundation for Statistical Computing, Vienna, Austria) consisted of a decision tree (short-term) and a state transition model (long-term) to calculate the mean expected costs and quality-adjusted life-years for people with ischaemic stroke and suspected large-vessel occlusion comparing artificial intelligence-derived software-assisted review to usual care. Results: A total of 22 studies (30 publications) were included in the review; 18/22 studies concerned artificial intelligence-derived software for the interpretation of computed tomography angiography to detect large-vessel occlusion. No study evaluated an artificial intelligence-derived software technology used as specified in the inclusion criteria for this assessment. For artificial intelligence-derived software technology alone, sensitivity and specificity estimates for proximal anterior circulation large-vessel occlusion were 95.4% (95% confidence interval 92.7% to 97.1%) and 79.4% (95% confidence interval 75.8% to 82.6%) for Rapid (iSchemaView, Menlo Park, CA, USA) computed tomography angiography, 91.2% (95% confidence interval 77.0% to 97.0%) and 85.0 (95% confidence interval 64.0% to 94.8%) for Viz LVO (Viz.ai, Inc., San Fransisco, VA, USA) large-vessel occlusion, 83.8% (95% confidence interval 77.3% to 88.7%) and 95.7% (95% confidence interval 91.0% to 98.0%) for Brainomix (Brainomix Ltd, Oxford, UK) e-computed tomography angiography and 98.1% (95% confidence interval 94.5% to 99.3%) and 98.2% (95% confidence interval 95.5% to 99.3%) for Avicenna CINA (Avicenna AI, La Ciotat, France) large-vessel occlusion, based on one study each. These studies were not considered appropriate to inform cost-effectiveness modelling but formed the basis by which the accuracy of artificial intelligence plus human reader could be elicited by expert opinion. Probabilistic analyses based on the expert elicitation to inform the sensitivity of the diagnostic pathway indicated that the addition of artificial intelligence to detect large-vessel occlusion is potentially more effective (quality-adjusted life-year gain of 0.003), more costly (increased costs of £8.61) and cost-effective for willingness-to-pay thresholds of £3380 per quality-adjusted life-year and higher. Limitations and conclusions: The available evidence is not suitable to determine the clinical effectiveness of using artificial intelligence-derived software to support the review of computed tomography brain scans in acute stroke. The economic analyses did not provide evidence to prefer the artificial intelligence-derived software strategy over current clinical practice. However, results indicated that if the addition of artificial intelligence-derived software-assisted review for guiding mechanical thrombectomy treatment decisions increased the sensitivity of the diagnostic pathway (i.e. reduced the proportion of undetected large-vessel occlusions), this may be considered cost-effective. Future work: Large, preferably multicentre, studies are needed (for all artificial intelligence-derived software technologies) that evaluate these technologies as they would be implemented in clinical practice. Study registration: This study is registered as PROSPERO CRD42021269609. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR133836) and is published in full in Health Technology Assessment; Vol. 28, No. 11. See the NIHR Funding and Awards website for further award information.

Stroke is a serious life-threatening medical condition caused by a blood clot or haemorrhage in the brain. Quick and effective management, including a brain scan, of the patients with suspected stroke can make a big difference in their outcome. Artificial intelligence-derived computer programmes exist that are intended to help with the interpretation of computed tomography scans of the brain in stroke. We undertook a thorough review of the existing research into the effectiveness and value for money of using these programmes to help doctors and other specialists to interpret computed tomography brain scans. We found very little evidence to tell us how well artificial intelligence-derived computer programmes work in practice. Some studies have looked at artificial intelligence-derived computer programmes on their own (i.e. not taken together with a doctor's judgement, as they were designed to be used). Other studies have looked at what happens to patients who are treated for stroke when artificial intelligence-derived computer programmes are used; these studies provide no information about whether using artificial intelligence-derived computer programmes may have led to patients who could have benefitted from treatment being missed. It is unclear how well artificial intelligence-derived software-assisted review works when added to current clinical practice.

Exploring the Cost Effectiveness of a Whole-Genome Sequencing-Based Biomarker for Treatment Selection in Patients with Advanced Lung Cancer Ineligible for Targeted Therapy.

OBJECTIVE: We aimed to perform an early cost-effectiveness analysis of using a whole-genome sequencing-based tumor mutation burden (WGS-TMB), instead of programmed death-ligand 1 (PD-L1), for immunotherapy treatment selection in patients with non-squamous advanced/metastatic non-small cell lung cancer ineligible for targeted therapy, from a Dutch healthcare perspective. METHODS: A decision-model simulating individual patients with metastatic non-small cell lung cancer was used to evaluate diagnostic strategies to select first-line immunotherapy only or the immunotherapy plus chemotherapy combination. Treatment was selected using PD-L1 [A, current practice], WGS-TMB [B], and both PD-L1 and WGS-TMB [C]. Strategies D, E, and F take into account a patient's disease burden, in addition to PD-L1, WGS-TMB, and both PD-L1 and WGS-TMB, respectively. Disease burden was defined as a fast-growing tumor, a high number of metastases, and/or weight loss. A threshold of 10 mutations per mega-base was used to classify patients into TMB-high and TMB-low groups. Outcomes were discounted quality-adjusted life-years (QALYs) and healthcare costs measured from the start of first-line treatment to death. Healthcare costs includes drug acquisition, follow-up costs, and molecular diagnostic tests (i.e., standard diagnostic techniques and/or WGS for strategies involving TMB). Results were reported using the net monetary benefit at a willingness-to-pay threshold of €80,000/QALY. Additional scenario and threshold analyses were performed. RESULTS: Strategy B had the lowest QALYs (1.84) and lowest healthcare costs (€120,800). The highest QALYs and healthcare costs were 2.00 and €140,400 in strategy F. In the base-case analysis, strategy A was cost effective with the highest net monetary benefit (€27,300), followed by strategy B (€26,700). Strategy B was cost effective when the cost of WGS testing was decreased by at least 24% or when immunotherapy results in an additional 0.5 year of life gained or more for TMB high compared with TMB low. Strategies C and F, which combined TMB and PD-L1 had the highest net monetary benefit (≥ €76,900) when the cost of WGS testing, immunotherapy, and chemotherapy acquisition were simultaneously reduced by at least 47%, 39%, and 43%, respectively. Furthermore, strategy C resulted in the highest net monetary benefit (≥ €39,900) in a scenario where patients with both PD-L1 low and TMB low were treated with chemotherapy instead of immunotherapy plus chemotherapy. CONCLUSIONS: The use of WGS-TMB is not cost effective compared to PD-L1 for immunotherapy treatment selection in non-squamous metastatic non-small cell lung cancer in the Netherlands. WGS-TMB could become cost effective provided there is a reduction in the cost of WGS testing or there is an increase in the predictive value of WGS-TMB for immunotherapy effectiveness. Alternatively, a combination strategy of PD-L1 testing with WGS-TMB would be cost effective if used to support the choice to withhold immunotherapy in patients with a low expected benefit of immunotherapy.

Comprehensive Review of Methods to Assess Uncertainty in Health Economic Evaluations.

Uncertainty assessment is a cornerstone in model-based health economic evaluations (HEEs) that inform reimbursement decisions. No comprehensive overview of available uncertainty assessment methods currently exists. We aimed to review methods for uncertainty assessment for use in model-based HEEs, by conducting a snowballing review. We categorised all methods according to their stage of use relating to uncertainty assessment (identification, analysis, communication). Additionally, we classified identification methods according to sources of uncertainty, and subdivided analysis and communication methods according to their purpose. The review identified a total of 80 uncertainty methods: 30 identification, 28 analysis, and 22 communication methods. Uncertainty identification methods exist to address uncertainty from different sources. Most identification methods were developed with the objective to assess related concepts such as validity, model quality, and relevance. Almost all uncertainty analysis and communication methods required uncertainty to be quantified and inclusion of uncertainties in probabilistic analysis. Our review can help analysts and decision makers in selecting uncertainty assessment methods according to their aim and purpose of the assessment. We noted a need for further clarification of terminology and guidance on the use of (combinations of) methods to identify uncertainty and related concepts such as validity and quality. A key finding is that uncertainty assessment relies heavily on quantification, which may necessitate increased use of expert elicitation and/or the development of methods to assess unquantified uncertainty.

A graphical analysis of aspects contributing to the spreading of measurements of left ventricular function.

The Simpson's method is the standard technique to determine left ventricular (LV) ejection fraction (EF) on echocardiography. The large inter-observer variability of measuring LVEF is well documented but not fully understood. A graphical analysis was used to elaborate what contributes to the inter-observer difference. Forty-two cardiologists (32 male, 39 ± 7 years) evaluated the LVEF using the Simpson's method on 15 different echocardiograms (2 and 4 chamber view (2CH/4CH)); the program did not show the result of EF to prevent a bias. End-diastolic (ED) and end-systolic (ES) frames were predefined ensuring measurement at the same time point of the cardiac cycles. After standardization of the LV contour, the differences of the individual contours compared to a reference contour were measured. Also, the spreading of lateral/medial mitral annulus contours and the apex were depicted. A significant spreading of LV-contours was seen with larger contours leading to higher EFs (p < 0.001). Experience did not influence the determination of LVEF. ED-volumes showed more spreading than ES-volumes ((3.6 mm (IQR: 2.6-4.0) vs. 3.4 mm (IQR: 2.8-3.8), p < 0.001). Also, the differences were larger for the 2CH compared to the 4CH (p < 0.001). Variability was significantly larger for lateral than septal wall (p < 0.001) as well as the anterior compared to the inferior wall (p < 0.001). There was a relevant scattering of the apex and medial/ lateral mitral annulus ring. There was a large variability of LV-volumes and LVEF as well as position of mitral valve ring and apex. There were global differences (apical 2CH or 4CH), regional aspects (LV walls) and temporal factors (ED vs. ES). Thus, multiple factors contributed to the large variability.Trial registration: The study was registered at "Netherlands Trial Register" ( www.trialregister.nl ; study number: NL5131).

Development and validation of a decision model for the evaluation of novel lung cancer treatments in the Netherlands.

Recent discoveries in molecular diagnostics and drug treatments have improved the treatment of patients with advanced (inoperable) non-squamous non-small cell lung cancer (NSCLC) from solely platinum-based chemotherapy to more personalized treatment, including targeted therapies and immunotherapies. However, these improvements come at considerable costs, highlighting the need to assess their cost-effectiveness in order to optimize lung cancer care. Traditionally, cost-effectiveness models for the evaluation of new lung cancer treatments were based on the findings of the randomized control trials (RCTs). However, the strict RCT inclusion criteria make RCT patients not representative of patients in the real-world. Patients in RCTs have a better prognosis than patients in a real-world setting. Therefore, in this study, we developed and validated a diagnosis-treatment decision model for patients with advanced (inoperable) non-squamous NSCLC based on real-world data in the Netherlands. The model is a patient-level microsimulation model implemented as discrete event simulation with five health events. Patients are simulated from diagnosis to death, including at most three treatment lines. The base-model (non-personalized strategy) was populated using real-world data of patients treated with platinum-based chemotherapy between 2008 and 2014 in one of six Dutch teaching hospitals. To simulate personalized care, molecular tumor characteristics were incorporated in the model based on the literature. The impact of novel targeted treatments and immunotherapies was included based on published RCTs. To validate the model, we compared survival under a personalized treatment strategy with observed real-world survival. This model can be used for health-care evaluation of personalized treatment for patients with advanced (inoperable) NSCLC in the Netherlands.

Headroom Analysis for Early Economic Evaluation: A Systematic Review.

OBJECTIVES: The headroom analysis is an early economic evaluation that quantifies the highest price at which an intervention may still be cost effective. Currently, there is no comprehensive review on how it is applied. This study investigated the application of the headroom analysis, specifically (1) how the headroom analysis is framed (2) the analytical approach and sources of evidence used, and (3) how expert judgement is used and reported. METHODS: A systematic search was conducted in PubMed, Embase, Web of Science, EconLit, and Google Scholar on 28 April 2022. Studies were eligible if they reported an application of the headroom analysis. Data were presented in tabular form and summarised descriptively. RESULTS: We identified 42 relevant papers. The headroom analysis was applied to medicines (29%), diagnostic or screening tests (29%), procedures, programmes and systems (21%), medical devices (19%), and a combined test and device (2%). All studies used model-based analyses, with 40% using simple models and 60% using more comprehensive models. Thirty-three percent of the studies assumed perfect effectiveness of the health technology, while 67% adopted realistic assumptions. Ten percent of the studies calculated an effectiveness-seeking headroom instead of a cost-seeking headroom. Expert judgement was used in 71% of the studies; 23 studies (55%) used expert opinion, 6 studies (14%) used expert elicitation, and 1 study (2%) used both. CONCLUSIONS: Because the application of the headroom analysis varies considerably, we recommend its appropriate use and clear reporting of analytical approaches, level of evidence available, and the use of expert judgement.

Fenfluramine for Treating Dravet Syndrome: An Evidence Review Group Perspective of a NICE Single Technology Appraisal.

Fenfluramine, tradename Fintepla®, was appraised within the National Institute for Health and Care Excellence (NICE) single technology appraisal (STA) process as Technology Appraisal 808. Within the STA process, the company (Zogenix International) provided NICE with a written submission and a mathematical health economic model, summarising the company's estimates of the clinical effectiveness and cost-effectiveness of fenfluramine for patients with Dravet syndrome (DS). This company submission (CS) was reviewed by an evidence review group (ERG) independent of NICE. The ERG, Kleijnen Systematic Reviews in collaboration with Maastricht University Medical Centre, produced an ERG report. This paper presents a summary of the ERG report and the development of the NICE guidance. The CS included a systematic review of the evidence for fenfluramine. From this review the company identified and presented evidence from two randomised trials (Study 1 and Study 1504), an open-label extension study (Study 1503) and 'real world evidence' from a prospective and retrospective study. Both randomised trials were conducted in patients up to 18 years of age with DS, whose seizures were incompletely controlled with previous anti-epileptic drugs. A Bayesian network meta-analysis was performed to compare fenfluramine with cannabidiol plus clobazam. There was no evidence of a difference between any doses of fenfluramine and cannabidiol in the mean convulsive seizure frequency (CSF) rate during treatment. However, fenfluramine increased the number of patients achieving ≥ 50% reduction in CSF frequency from baseline compared to cannabidiol. The company used an individual-patient state-transition model (R version 3.5.2) to model cost-effectiveness of fenfluramine. The CSF and convulsive seizure-free days were estimated using patient-level data from the placebo arm of the fenfluramine registration studies. Subsequently, a treatment effect of either fenfluramine or cannabidiol was applied. Utility values for the economic model were obtained by mapping Pediatric Quality of Life Inventory data from the registration studies to EuroQol-5D-3L Youth (EQ-5D-Y-3L). The company included caregiver utilities in their base-case, as the severe needs of patients with DS have a major impact on parents and caregivers. There were several key issues. First, the company included caregiver utilities in the model in a way that when patients in the economic model died, the corresponding caregiver utility was also set to zero. Second, the model was built in R statistical software, resulting in transparency issues. Third, the company assumed the same percentage reduction for convulsive seizure days as was estimated for CSF. Fourth, during the final appraisal committee meeting, influential changes were made to the model that were not in line with the ERG's preferences (but were accepted by the appraisal committee). The company's revised and final incremental cost effectiveness ratio (ICER) in line with committee preferences resulted in fenfluramine dominating cannabidiol. Fenfluramine was recommended as an add-on to other antiepileptic medicines for treating seizures associated with DS in people aged 2 years and older in the National Health Service (NHS).

Cost-Effectiveness and Budget Impact of Future Developments With Whole-Genome Sequencing for Patients With Lung Cancer.

OBJECTIVES: This study aimed to investigate the cost-effectiveness, budget impact (BI), and impact of uncertainty of future developments concerning whole-genome sequencing (WGS) as a clinical diagnostic test compared with standard of care (SoC) in patients with locally advanced and metastatic non-small cell lung cancer. METHODS: A total of 3 likely scenarios to take place within 5 years (according to experts) were simulated using a previously developed, peer reviewed, and published decision model. The scenarios concerned "WGS results used for treatment selection" (scenario 1), "WGS-based biomarker for immunotherapy" (scenario 2), and "off-label drug approval for WGS results" (scenario 3). Two diagnostic strategies of the original model, "SoC" and "WGS as a diagnostic test" (base model), were used to compare our scenarios with. Outcomes were reported for the base model, all scenarios separately, combined (combined unweighted), and weighted by likelihood (combined weighted). Cost-effectiveness, BI, and value of information analyses were performed for WGS compared with SoC. RESULTS: Total costs and quality-adjusted life-years for SoC in metastatic non-small cell lung cancer were €149 698 and 1.235. Incremental outcomes of WGS were €1529/0.002(base model), -€222/0.020(scenario 1), -€2576/0.023(scenario 2), €388/0.024(scenario 3), -€5041/0.060(combined unweighted), and -€1715/0.029(combined weighted). The annual BI for adopting WGS for this population in The Netherlands ranged between €682 million (combined unweighted) and €714 million (base model). The consequences of uncertainty amounted to €3.4 million for all scenarios (combined weighted) and to €699 000 for the diagnostic yield of WGS alone (combined weighted). CONCLUSIONS: Our findings suggest that it is likely for WGS to become cost-effective within the near future if it identifies more patients with actionable targets and show the impact of uncertainty regarding its diagnostic yield. Modeling future scenarios can be useful to consider early adoption of WGS while timely anticipating on unforeseen developments before final conclusions are reached.

Belimumab for Treating Active Autoantibody-Positive Systemic Lupus Erythematosus: An Evidence Review Group Perspective of a NICE Single Technology Appraisal.

As part of its Single Technology Appraisal (STA) process, the National Institute for Health and Care Excellence (NICE) invited the manufacturer (GlaxoSmithKline [GSK]) of Benlysta (belimumab) to submit evidence regarding its clinical and cost effectiveness, for the review and possible extension of a previously conditionally approved intravenous formulation of belimumab for the treatment of active autoantibody-positive systemic lupus erythematosus (SLE). Kleijnen Systematic Reviews Ltd, in collaboration with Maastricht University Medical Centre+, was commissioned to act as the independent Evidence Review Group (ERG). This paper summarises the company submission (CS), presents the ERG's critical review of the clinical and cost-effectiveness evidence in the CS, highlights the key methodological considerations, and describes the development of the NICE guidance by the NICE Appraisal Committee.This appraisal is different to the previous appraisal in three ways: (1). This appraisal expands its definition of 'high disease activity'. (2). In TA397, belimumab was approved, with a managed access arrangement (MAA), for adults only. This appraisal includes subjects aged 5 years or older. (3). The original appraisal included an intravenous formulation only, but the current appraisal also includes a new subcutaneous formulation in the form of a prefilled pen.The company was required to collect real-world data from the British Isles Lupus Assessment Group Biologics Register (BILAG-BR), including data on the efficacy, safety, and effect on health-related quality of life of belimumab versus rituximab. This appraisal considers these data as well as additional clinical trial evidence presented in the company's updated submission to address uncertainties identified during the original appraisal. The ERG identified three major concerns with the evidence presented on the clinical effectiveness in the current submission; namely, short follow-up in the main comparative trials (BLISS-SC, BLISS-52 and BLISS-76); using the propensity score-matching (PSM) analysis in calibrating the cost-effectiveness model can severely bias the results in favour of belimumab; and BILAG-BR data are not suitable for a comparison of belimumab with rituximab.The main issue in the economic analysis was the uncertainty about long-term disease activity progression and resulting organ damage. The company's approach of calibrating modelled organ damage to longer-term data analysed using the PSM analysis was methodologically inappropriate. The final analysis comparing belimumab with standard treatment for the intravenous formulation resulted in an incremental cost-effectiveness ratio of £12,335 per quality-adjusted life-year (QALY) gained and £30,278 per QALY gained in the company's and ERG's base-case analyses, respectively. For the subcutaneous formulation, the final analysis resulted in £8480 per QALY gained and £29,313 per QALY gained in the company's and ERG's base-case analyses, respectively. NICE recommended belimumab in both intravenous and subcutaneous formulations as an add-on treatment option for active autoantibody-positive SLE in the HDA-2 subgroup.

Cost-effectiveness of prophylactic cranial irradiation in stage III non-small cell lung cancer.

INTRODUCTION: In stage III non-small cell lung cancer (NSCLC), prophylactic cranial irradiation (PCI) reduces the brain metastases incidence and prolongs the progression-free survival without improving overall survival. PCI increases the risk of toxicity and is currently not adopted in routine care. Our objective was to assess the cost-effectiveness of PCI compared with no PCI in stage III NSCLC from a Dutch societal perspective. METHODS: A cohort partitioned survival model was developed based on individual patient data from three randomized phase III trials (N = 670). Quality-adjusted life years (QALYs) and costs were estimated over a lifetime time horizon. A willingness-to-pay (WTP) threshold of €80,000 per QALY was adopted. Sensitivity and scenario analyses were performed to address parameter uncertainty and to explore what parameters had the greatest impact on the cost-effectiveness results. RESULTS: PCI was more effective and costly (0.443 QALYs, €10,123) than no PCI, resulting in an incremental cost-effectiveness ratio (ICER) of €22,843 per QALY gained. The probability of PCI being cost-effective at a WTP threshold of €80,000 per QALY was 93%. The probability of PCI gaining three and six additional months of life were 76% and 56%. The scenario analysis adding durvalumab increased the ICER to €35,159 per QALY gained. Using alternative survival distributions had little impact on the ICER. Assuming fewer PCI fractions and excluding indirect costs decreased the ICER to €18,263 and €5554 per QALY gained. CONCLUSION: PCI is cost-effective compared to no PCI in stage III NSCLC, and could therefore, from a cost-effectiveness perspective, be considered in routine care.

Association of different fractionation schedules for prophylactic cranial irradiation with toxicity and brain metastases-free survival in stage III non-small cell lung cancer: A pooled analysis of individual patient data from three randomized trials.

We assessed the impact of different PCI fractionation schedules (30 Gy in 10 versus 15 fractions) on brain metastases-free survival (BMFS) and toxicity in stage III NSCLC. Our results suggest that 30 Gy in 10 fractions is associated with increased toxicity, while no conclusive evidence of improving BMFS was seen with this schedule.

Early Cost Effectiveness of Whole-Genome Sequencing as a Clinical Diagnostic Test for Patients with Inoperable Stage IIIB,C/IV Non-squamous Non-small-Cell Lung Cancer.

BACKGROUND: Advanced non-small-cell lung cancer (NSCLC) harbours many genetic aberrations that can be targeted with systemic treatments. Whole-genome sequencing (WGS) can simultaneously detect these (and possibly new) molecular targets. However, the exact added clinical value of WGS is unknown. OBJECTIVE: The objective of this study was to determine the early cost effectiveness of using WGS in diagnostic strategies compared with currently used molecular diagnostics for patients with inoperable stage IIIB,C/IV non-squamous NSCLC from a Dutch healthcare perspective. METHODS: A decision tree represented the diagnostic pathway, and a cohort state transition model represented disease progression. Three diagnostic strategies were modelled: standard of care (SoC) alone, WGS as a diagnostic test, and SoC followed by WGS. Treatment effectiveness was based on a systematic review. Probabilistic cost-effectiveness analyses were performed, and threshold analyses (using €80,000 per quality-adjusted life-year [QALY]) was used to explore the early cost effectiveness of WGS. RESULTS: WGS as a diagnostic test resulted in more QALYs (0.002) and costs (€1534 [incremental net monetary benefit -€1349]), and SoC followed by WGS resulted in fewer QALYs (-0.002) and more costs (€1059 [-€1194]) compared with SoC alone. WGS as a diagnostic test was only cost effective if it was priced at €2000 per patient and identified 2.7% more actionable patients than SoC alone. Treating these additional identified patients with new treatments costing >€4069 per month decreased the probability of cost effectiveness. CONCLUSIONS: Our analysis suggests that providing WGS as a diagnostic test is cost effective compared with SoC followed by WGS and SoC alone if costs for WGS decrease and additional patients with actionable targets are identified. This cost-effectiveness model can be used to incorporate new findings iteratively and to support ongoing decision making regarding the use of WGS in this rapidly evolving field.

Implementation Barriers to Value of Information Analysis in Health Technology Decision Making: Results From a Process Evaluation.

OBJECTIVES: Value of information (VOI) analysis can support health technology assessment decision making, but it is a long way from being standard use. The objective of this study was to understand barriers to the implementation of VOI analysis and propose actions to overcome these. METHODS: We performed a process evaluation of VOI analysis use within decision making on tomosynthesis versus digital mammography for use in the Dutch breast cancer population screening. Based on steering committee meeting attendance and regular meetings with analysts, we developed a list of barriers to VOI use, which were analyzed using an established diffusion model. We proposed actions to address these barriers. Barriers and actions were discussed and validated in a workshop with stakeholders representing patients, clinicians, regulators, policy advisors, researchers, and the industry. RESULTS: Consensus was reached on groups of barriers, which included characteristics of VOI analysis itself, stakeholder's attitudes, analysts' and policy makers' skills and knowledge, system readiness, and implementation in the organization. Observed barriers did not only pertain to VOI analysis itself but also to formulating the objective of the assessment, economic modeling, and broader aspects of uncertainty assessment. Actions to overcome these barriers related to organizational changes, knowledge transfer, cultural change, and tools. CONCLUSIONS: This in-depth analysis of barriers to implementation of VOI analysis and resulting actions and tools may be useful to health technology assessment organizations that wish to implement VOI analysis in technology assessment and research prioritization. Further research should focus on application and evaluation of the proposed actions in real-world assessment processes.

Exploring the Feasibility of Comprehensive Uncertainty Assessment in Health Economic Modeling: A Case Study.

OBJECTIVES: Decision makers adopt health technologies based on health economic models that are subject to uncertainty. In an ideal world, these models parameterize all uncertainties and reflect them in the cost-effectiveness probability and risk associated with the adoption. In practice, uncertainty assessment is often incomplete, potentially leading to suboptimal reimbursement recommendations and risk management. This study examines the feasibility of comprehensive uncertainty assessment in health economic models. METHODS: A state transition model on peripheral arterial disease treatment was used as a case study. Uncertainties were identified and added to the probabilistic sensitivity analysis if possible. Parameter distributions were obtained by expert elicitation, and structural uncertainties were either parameterized or explored in scenario analyses, which were model averaged. RESULTS: A truly comprehensive uncertainty assessment, parameterizing all uncertainty, could not be achieved. Expert elicitation informed 8 effectiveness, utility, and cost parameters. Uncertainties were parameterized or explored in scenario analyses and with model averaging. Barriers included time and resource constraints, also of clinical experts, and lacking guidance regarding some aspects of expert elicitation, evidence aggregation, and handling of structural uncertainty. The team's multidisciplinary expertise and existing literature and tools were facilitators. CONCLUSIONS: While comprehensive uncertainty assessment may not be attainable, improvements in uncertainty assessment in general are no doubt desirable. This requires the development of detailed guidance and hands-on tutorials for methods of uncertainty assessment, in particular aspects of expert elicitation, evidence aggregation, and handling of structural uncertainty. The issue of benefits of uncertainty assessment versus time and resources needed remains unclear.