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.

Optimizing the Use of High-Sensitivity Troponin Assays for the Early Rule-out of Myocardial Infarction in Patients Presenting with Chest Pain: A Systematic Review.

BACKGROUND: We assessed the accuracy and clinical effectiveness of high-sensitivity cardiac troponin (hs-cTn) assays for early rule-out of non-ST-segment elevation myocardial infarction (NSTEMI) in adults presenting with acute chest pain. METHODS: Sixteen databases were searched to September 2019. Review methods followed published guidelines. The bivariate model was used to estimate summary sensitivity and specificity with 95% confidence intervals for meta-analyses involving 4 or more studies, otherwise random-effects logistic regression was used. RESULTS: Thirty-seven studies (124 publications) were included in the review. The hs-cTn test strategies evaluated in the included studies were defined by the combination of 4 factors (assay, number of tests, timing of tests, and threshold concentration or change in concentration between tests). Clinical opinion indicated a minimum acceptable sensitivity of 97%. A single test at presentation using a threshold at or near the assay limit of detection could reliably rule-out NSTEMI for a range of hs-cTn assays. Serial testing strategies, which include an immediate rule-out step, increased the proportion ruled out without loss of sensitivity. Finally, serial testing strategies without an immediate rule-out step had excellent sensitivity and specificity, but at the expense of the option for immediate patient discharge. CONCLUSION: Test strategies that comprise an initial rule-out step, based on low hs-cTn concentrations at presentation and a minimum symptom duration, and a second step for those not ruled-out that incorporates a small absolute change in hs-cTn at 1, 2, or 3 hours, produce the highest rule-out rates with a very low risk of missed NSTEMI. PROSPERO REGISTRATION: CRD42019154716.

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.

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.

Direct Medical Costs of Advanced Breast Cancer Treatment: A Real-World Study in the Southeast of The Netherlands.

OBJECTIVES: Policy makers increasingly seek to complement data from clinical trials with information from routine care. This study aims to provide a detailed account of the hospital resource use and associated costs of patients with advanced breast cancer in The Netherlands. METHODS: Data from 597 patients with advanced breast cancer, diagnosed between 2010 and 2014, were retrieved from the Southeast Netherlands Advanced Breast Cancer Registry. Database lock for this study was in October 2017. We report the observed hospital costs for different resource categories and the lifetime costs per patient, adjusted for censoring using Lin's method. The relationship between patients' characteristics and costs was studied using multivariable regression. RESULTS: The average (SE) lifetime hospital costs of patients with advanced breast cancer were €52 709 (405). Costs differed considerably between patient subgroups, ranging from €29 803 for patients with a triple-negative subtype to €92 272 for patients with hormone receptor positive and human epidermal growth factor receptor 2 positive cancer. Apart from the cancer subtype, several other factors, including age and survival time, were independently associated with patient lifetime costs. Overall, a large share of costs was attributed to systemic therapies (56%), predominantly to a few expensive agents, such as trastuzumab (15%), everolimus (10%), and bevacizumab (9%), as well as to inpatient hospital days (20%). CONCLUSIONS: This real-world study shows the high degree of variability in hospital resource use and associated costs in advanced breast cancer care. The presented resource use and costs data provide researchers and policy makers with key figures for economic evaluations and budget impact analyses.

An economic evaluation of eribulin for advanced breast cancer treatment based on the Southeast Netherlands advanced breast cancer registry.

Background: In 2013, eribulin was reimbursed under a coverage with evidence development (CED) as third or later chemotherapy line for advanced breast cancer (ABC) patients in the Netherlands because of uncertain cost effectiveness. In 2016, the final decision of reimbursing eribulin was taken without considering the evidence collected during CED research. We analysed the cost effectiveness of eribulin versus non-eribulin chemotherapy, using real-world data.Methods: A three health states (progression-free, progressed disease, dead) partitioned survival model was developed. The SOuth East Netherlands Advanced BREast Cancer (SONABRE) registry informed the effectiveness and costs inputs. Health state utility values were obtained from the literature. Incremental cost-effectiveness ratio (ICER) between the eribulin and matched non-eribulin chemotherapy was estimated. Deterministic and probabilistic sensitivity analyses and scenario analyses were performed. The financial risk (i.e., the expected value of perfect information (EVPI) plus the expected monetary loss (eML) associated with reimbursing eribulin) and budget impact associated with reimbursing eribulin were calculated.Results: Eribulin led to higher health benefits (0.07 quality-adjusted life year (QALY)) and costs (€15,321) compared with non-eribulin chemotherapy. This resulted in an ICER of €220,608. At a €80,000 per QALY threshold, the risk of reimbursing eribulin was €9,791 per patient (EVPI €13, eML €9,778). Scaled up to the Dutch population, the estimated annual budget impact was €1.9 million and the annual risk of reimbursing eribulin was €2.7 million.Conclusion: From a Dutch societal perspective, eribulin is not cost effective when considering its list price as third and later chemotherapy line for ABC patients.

Quality of life in a real-world cohort of advanced breast cancer patients: a study of the SONABRE Registry.

PURPOSE: We aimed to evaluate quality of life (QoL) using the European Quality of Life Five-Dimensions questionnaire (EQ-5D-3L) in a real-world cohort of Dutch advanced breast cancer (ABC) patients. Secondary, we reported differences in QoL between subgroups of patients based on age, comorbidity, tumor-, and treatment characteristics, and assessed the association of duration of metastatic disease and time to death with QoL. METHODS: ABC patients who attended the outpatient clinic between October 2010 and May 2011 were asked to fill out the EQ-5D-3L questionnaire. Patient-, disease-, and treatment characteristics were obtained from the medical files. Health-utility scores were calculated. Subgroups were described and compared for utility scores by parametric and non-parametric methods. RESULTS: A total of 92 patients were included with a median utility score of 0.691 (Interquartile range [IQR] 0.244). Patients ≥ 65 years had significantly worse median utility scores than younger patients; 0.638 versus 0.743, respectively (p = 0.017). Moreover, scores were significantly worse for patients with versus those without comorbidity (medians 0.620 versus 0.725, p = 0.005). Utility scores did not significantly differ between subgroups of tumor type, type of systemic treatment, number of previous palliative treatment(s), or number or location of metastatic site(s). The remaining survival was correlated with utility scores (correlation coefficient (r) = 0.260, p = 0.0252), especially in the subgroup < 65 years (r = 0.340, p = 0.0169), whereas there was no significant correlation with time since metastatic diagnosis (r = - 0.106, p = 0.3136). CONCLUSION: Within this real-world cross-sectional study, QoL was significantly associated with age, comorbidity, and remaining survival duration. The observation of a lower QoL in ABC patients, possibly indicating the last period of life, may assist clinical decision-making on timing of cessation of systemic antitumor therapy.

Uncertainty and Coverage With Evidence Development: Does Practice Meet Theory?

OBJECTIVES: In theory, a successful coverage with evidence development (CED) scheme is one that addresses the most important uncertainties in a given assessment. We investigated the following: (1) which uncertainties were present during the initial assessment of 3 Dutch CED cases, (2) how these uncertainties were integrated in the initial assessments, (3) whether CED research plans included the identified uncertainties, and (4) issues with managing uncertainty in CED research and ways forward from these issues. METHODS: Three CED initial assessment dossiers were analyzed and 16 stakeholders were interviewed. Uncertainties were identified in interviews and dossiers and were categorized in different causes: unavailability, indirectness, and imprecision of evidence. Identified uncertainties could be mentioned, described, and explored. Issues and ways forward to address uncertainty in CED schemes were discussed during the interviews. RESULTS: Forty-two uncertainties were identified. Thirteen (31%) were caused by unavailability, 17 (40%) by indirectness, and 12 (29%) by imprecision. Thirty-four uncertainties (81%) were only mentioned, 19 (45%) were described, and the impact of 3 (7%) uncertainties on the results was explored in the assessment dossiers. Seventeen uncertainties (40%) were included in the CED research plans. According to stakeholders, research did not address the identified uncertainty, but CED research should be designed to focus on these. CONCLUSIONS: In practice, uncertainties were neither systematically nor completely identified in the analyzed CED schemes. A framework would help to systematically identify uncertainty, and this process should involve all stakeholders. Value of information analysis, and the uncertainties that are not included in this analysis should inform CED research design.

Comparison of three generic quality-of-life metrics in peripheral arterial disease patients undergoing conservative and invasive treatments.

PURPOSE: To determine the effect of revascularisation for peripheral arterial disease (PAD) on QoL in the first and second year following diagnosis, to compare the effect depicted by Short Form Six Dimensions (SF-6D) and EuroQoL five Dimensions (EQ-5D) utilities, and Visual Analogue Scale (VAS) scores and to analyse heterogeneity in treatment response. METHODS: Longitudinal data from 229 PAD patients were obtained in an observational study in southern Netherlands. Utility scores were calculated with the international (SF-6D) and Dutch (EQ-5D) tariffs. We analysed treatment effect at years 1 and 2 through propensity score-matched ANCOVAs. Thereby, we estimated the marginal means (EMMs) of revascularisation and conservative treatment, and identified covariates of revascularisation effect. RESULTS: A year after diagnosis, 70 patients had been revascularised; the EMMs of revascularisation were 0.038, 0.077 and 0.019 for SF-6D, EQ-5D and VAS, respectively (always in this order). For conservative treatment these were - 0.017, 0.038 and 0.021. At 2-year follow-up, the EMMs of revascularisation were 0.015, 0.077 and 0.027, for conservative treatment these were - 0.020, 0.013 and - 0.004. Baseline QoL (and rest pain in year 2) were covariates of treatment effect. CONCLUSIONS: We measured positive effects of revascularisation and conservative treatment on QoL a year after diagnosis, the effect of revascularisation was sustained over 2 years. The magnitude of effect varied between the metrics and was largest for the EQ-5D, which may be most suitable for QoL measurement in PAD patients. Baseline QoL influenced revascularisation effect, in clinical practice this may inform expected QoL gain in individual patients.

Development of an isotoxic decision support system integrating genetic markers of toxicity for the implantation of a rectum spacer.

INTRODUCTION: Previous studies revealed that dose escalated radiotherapy for prostate cancer patients leads to higher tumor control probabilities (TCP) but also to higher rectal toxicities. An isotoxic model was developed to maximize the given dose while controlling the toxicity level. This was applied to analyze the effect of an implantable rectum spacer (IRS) and extended with a genetic test of normal tissue radio-sensitivity. A virtual IRS (V-IRS) was tested using this method. We hypothesized that the patients with increased risk of toxicity would benefit more from an IRS. MATERIAL AND METHODS: Sixteen localized prostate cancer patients implanted with an IRS were included in the study. Treatment planning was performed on computed tomography (CT) images before and after the placement of the IRS and with a V-IRS. The normal tissue complication probability (NTCP) was calculated using a QUANTEC reviewed model for Grade > =2 late rectal bleeding and the number of fractions of the plans were adjusted until the NTCP value was under 5%. The resulting treatment plans were used to calculate the TCP before and after placement of an IRS. This was extended by adding the effect of two published genetic single nucleotide polymorphisms (SNP's) for late rectal bleeding. RESULTS: The median TCP resulting from the optimized plans in patients before the IRS was 75.1% [32.6-90.5%]. With IRS, the median TCP is significantly higher: 98.9% [80.8-99.9%] (p < .01). The difference in TCP between the V-IRS and the real IRS was 1.8% [0.0-18.0%]. Placing an IRS in the patients with SNP's improved the TCP from 49.0% [16.1-80.8%] and 48.9% [16.0-72.8%] to 96.3% [67.0-99.5%] and 90.1% [49.0-99.5%] (p < .01) respectively for either SNP. CONCLUSION: This study was a proof-of-concept for an isotoxic model with genetic biomarkers with a V-IRS as a multifactorial decision support system for the decision of a placement of an IRS.

Reviewing the quality, health benefit and value for money of chemotherapy and targeted therapy for metastatic breast cancer.

PURPOSE: To provide an overview of model characteristics and outcomes of model-based economic evaluations concerning chemotherapy and targeted therapy (TT) for metastatic breast cancer (MBC); to assess the quality of the studies; to analyse the association between model characteristics and study quality and outcomes. METHODS: PubMED and NHS EED were systematically searched. Inclusion criteria were as follows: English or Dutch language, model-based economic evaluation, chemotherapy or TT as intervention, population diagnosed with MBC, published between 2000 and 2014, reporting life years (LY) or quality-adjusted life-year (QALY) and an incremental cost-effectiveness ratio. General characteristics, model characteristics and outcomes of the studies were extracted. Quality of the studies was assessed through a checklist. RESULTS: 24 studies were included, considering 50 comparisons (20 concerning chemotherapy and 30 TT). Seven comparisons were represented in multiple studies. A health state-transition model including the following health states: stable/progression-free disease, progression and death was used in 18 studies. Studies fulfilled on average 14 out of the 26 items of the quality checklist, mostly due to a lack of transparency in reporting. Thirty-one per cent of the incremental net monetary benefit was positive. TT led to higher iQALY gained, and industry-sponsored studies reported more favourable cost-effectiveness outcomes. CONCLUSIONS: The development of a disease-specific reference model would improve the transparency and quality of model-based cost-effectiveness assessments for MBC treatments. Incremental health benefits increased over time, but were outweighed by the increased treatment costs. Consequently, increased health benefits led to lower value for money.

Cost-Effectiveness of Reduced Waiting Time for Head and Neck Cancer Patients due to a Lean Process Redesign.

BACKGROUND: Compared with new technologies, the redesign of care processes is generally considered less attractive to improve patient outcomes. Nevertheless, it might result in better patient outcomes, without further increasing costs. Because early initiation of treatment is of vital importance for patients with head and neck cancer (HNC), these care processes were redesigned. OBJECTIVES: This study aimed to assess patient outcomes and cost-effectiveness of this redesign. METHODS: An economic (Markov) model was constructed to evaluate the biopsy process of suspicious lesion under local instead of general anesthesia, and combining computed tomography and positron emission tomography for diagnostics and radiotherapy planning. Patients treated for HNC were included in the model stratified by disease location (larynx, oropharynx, hypopharynx, and oral cavity) and stage (I-II and III-IV). Probabilistic sensitivity analyses were performed. RESULTS: Waiting time before treatment start reduced from 5 to 22 days for the included patient groups, resulting in 0.13 to 0.66 additional quality-adjusted life-years. The new workflow was cost-effective for all the included patient groups, using a ceiling ratio of €80,000 or €20,000. For patients treated for tumors located at the larynx and oral cavity, the new workflow resulted in additional quality-adjusted life-years, and costs decreased compared with the regular workflow. The health care payer benefited €14.1 million and €91.5 million, respectively, when individual net monetary benefits were extrapolated to an organizational level and a national level. CONCLUSIONS: The redesigned care process reduced the waiting time for the treatment of patients with HNC and proved cost-effective. Because care improved, implementation on a wider scale should be considered.

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.

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.

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.

How should we deal with patient heterogeneity in economic evaluation: a systematic review of national pharmacoeconomic guidelines.

OBJECTIVE: To review and analyze recommendations from national pharmacoeconomic guidelines with regard to acknowledging patient heterogeneity in economic evaluations. METHODS: National pharmacoeconomic guidelines were obtained through the ISPOR Web site. Guidance was extracted by using a developed data extraction sheet. Extracted data were divided into subcategories on the basis of consensus meetings. RESULTS: Of the 26 included guidelines, 20 (77%) advised to identify patient heterogeneity. Most guidelines (77%) provided general methodological advice to acknowledge patient heterogeneity, including justifications for distinguishing subgroups (65%), prespecification of subgroups (42%), or methodology to acknowledge patient heterogeneity (77%). Stratified analysis of cost-effectiveness was most commonly advised (20 guidelines; 77%); however, guidance on the specific application of methods was scarce (9 guidelines; 34%) and generally limited if provided. Guidance to present patient heterogeneity was provided by 15 guidelines (58%), most prominently to describe the definition (31%) and justification (31%) of subgroups. CONCLUSIONS: The majority of national pharmacoeconomic guidelines provide guidance on acknowledging patient heterogeneity in economic evaluations. However, because guidance is mostly not specific, its usefulness is limited. This may reflect that the importance of acknowledging patient heterogeneity is usually recognized while there is a lack of consensus on specific methods to acknowledge patient heterogeneity. We advise the further development of national pharmacoeconomic guidelines to provide specific guidance on the identification of patient heterogeneity, methods to acknowledge it, and presenting the results. We present a checklist that can assist in formulating these recommendations. This could facilitate the systematic and transparent handling of patient heterogeneity in economic evaluations worldwide.

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.

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.