Value of Information Analysis for Research Decisions-An Introduction: Report 1 of the ISPOR Value of Information Analysis Emerging Good Practices Task Force

A Systematic Literature Review of Modelling Approaches to Evaluate the Cost Effectiveness of PET/CT for Therapy Response Monitoring in Oncology

BACKGROUND AND OBJECTIVE

This systematic literature review addresses model-based cost-effectiveness studies for therapy response monitoring with positron emission tomography (PET) generally combined with low-dose computed tomography (CT) for various cancer types. Given the known heterogeneity in therapy response events, studies should consider patient-level modelling rather than cohort-based modelling because of its flexibility in handling these events and the time to events. This review aims to identify the modelling methods used and includes a systematic assessment of the assumptions made in the current literature.

METHODS

This study was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Information sources included electronic bibliographic databases, reference lists of review articles and contact with experts in the fields of nuclear medicine, health technology assessment and health economics. Eligibility criteria included peer-reviewed scientific publications and published grey literature. Literature searches, screening and critical appraisal were conducted by two reviewers independently. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) were used to assess the methodological quality. The Bias in Economic Evaluation (ECOBIAS) checklist was used to determine the risk of bias in the included publications.

RESULTS

The search results included 2959 publications. The number of publications included for data extraction and synthesis was ten, representing eight unique studies. These studies addressed patients with lymphoma, advanced head and neck cancers, brain tumours, non-small cell lung cancer and cervical cancer. All studies addressed response to chemotherapy. No study evaluated response to immunotherapy. Most studies positioned PET/CT as an add-on modality and one study positioned PET/CT as a replacement for conventional imaging (X-ray and contrast-enhanced CT). Three studies reported decision-tree structures, four studies reported cohort-level state-transition models and one study reported a partitioned survival model. No patient-level models were reported. The simulation horizons adopted ranged from 1 year to lifetime. Most studies reported a probabilistic analysis, whereas two studies reported a deterministic analysis only. Two studies conducted a value of information analysis. Multiple studies did not adequately discuss model-specific aspects of bias. Most importantly and regularly observed were a high risk of structural assumptions bias, limited simulation horizon bias and wrong model bias.

CONCLUSIONS

Model-based cost-effectiveness analysis for therapy response monitoring with PET/CT was based on cohorts of patients instead of individual patients in the current literature. Therefore, the heterogeneity in therapy response events was commonly not addressed appropriately. Further research should include more advanced and patient-level modelling approaches to accurately represent the complex context of clinical practice and, therefore, to be meaningful to support decision making.

REGISTRATION

This review is registered in PROSPERO, the international prospective register of systematic reviews funded by the National Institute for Health Research, with CRD42023402581.

A Tutorial on Value-Based Adaptive Designs: Could a Value-Based Sequential 2-Arm Design Have Created More Health Economic Value for the Big CACTUS Trial?

OBJECTIVES

Value-based trials aim to maximize the expected net benefit by balancing technology adoption decisions and clinical trial costs. Adaptive trials offer additional efficiency. This article provides guidance on determining whether a value-based sequential design is the best option for an adaptive 2-arm trial, illustrated through a case study.

METHODS

We outlined 4 steps for the value-based sequential approach. The case study re-evaluates the Big CACTUS trial design using pilot trial data and a model-based health economic analysis. Expected net benefit is computed for (1) original fixed design, (2) value-based design with fixed sample size, and (3) optimal value-based sequential design with adaptive stopping. We compare pretrial modeling with the actual Big CACTUS trial results.

RESULTS

Over 10 years, the adoption decision would affect approximately 215 378 patients. Pretrial modeling shows that the expected net benefit minus costs are (1) £102 million for the original fixed design, (2) £107 million (+5.3% higher) for the value-based design with optimal fixed sample size, and (3) £109 million (+6.7% higher) for the optimal value-based sequential design with maximum sample size of 435 per arm. Post hoc analysis using actual Big CACTUS trial data indicates that the value-adaptive trial with a maximum sample size of 95 participant pairs would not have stopped early. Bootstrap simulations reveal a 9.76% probability of early completion with n = 95 pairs compared with 31.50% with n = 435 pairs.

CONCLUSIONS

The 4-step approach to value-based sequential 2-arm design with adaptive stopping was successfully implemented. Further application of value-based adaptive approaches could be useful to assess the efficiency of alternative study designs.

Net Monetary Benefit Lines Augmented with Value-of-Information Measures to Present the Results of Economic Evaluations under Uncertainty

BACKGROUND

Methods to present the result of cost-effectiveness analyses under parameter uncertainty include cost-effectiveness planes (CEPs), cost-effectiveness acceptability curves/frontier (CEACs/CEAF), expected loss curves (ELCs), and net monetary benefit (NMB) lines. We describe how NMB lines can be augmented to present NMB values that could be achieved by reducing or resolving parameter uncertainty. We evaluated the ability of these methods to correctly 1) identify the alternative with the highest expected NMB and 2) communicate the magnitude of parameter and decision uncertainty.

METHODS

We considered 4 hypothetical decision problems representing scenarios with high variance or correlated cost and effect estimates and alternatives with similar cost-effectiveness ratios. We used these decision problems to demonstrate the limitations of existing methods and the potential of augmented NMB lines to resolve these issues.

RESULTS

CEPs and CEACs/CEAF could falsely imply the lack of sufficient evidence to identify the optimal option if cost and effect estimates have high variance, are correlated across alternatives, or when alternatives have similar cost-effectiveness ratios. The augmented NMB lines and ELCs can correctly identify the option with the highest expected NMB and communicate the potential benefit of resolving uncertainties. Like ELCs, the augmented NMB lines provide information about the value of resolving parameter uncertainties, but augmented NMB lines may be easier to interpret for decision makers.

CONCLUSIONS

Our analysis supports recommending the augment NMB lines as an important method to present the results of economic evaluation studies under parameter uncertainty.

HIGHLIGHTS

The results of cost-effectiveness analyses (CEAs) when the cost and effect estimates of alternatives are uncertain are commonly presented using cost-effectiveness planes (CEPs), cost-effectiveness acceptability curves/frontier (CEACs/CEAF), and expected loss curves (ELCs).Although currently not often used, net monetary benefit (NMB) lines could present the results of cost-effectiveness to identify the alternative with the highest expected NMB values given the current level of uncertainty. Furthermore, NMB lines can be augmented to 1) show metrics of value of information, which measure the value of additional research to reduce or eliminate the decision uncertainty, and 2) display the confidence intervals along the NMB lines to ensure that NMB values are estimated accurately using a sufficiently large number of parameter samples.Using several decision problems, we demonstrate the limitation of existing methods to present the results of CEAs under parameter uncertainty and how augmented NMB lines could resolve these issues.Our analysis supports recommending augmented NMB lines as an important method to present the results of CEA under uncertainty since they 1) correctly identify the alternative with the highest expected NMB value given the current evidence, 2) provide information about the potential value of additional research to improve the decision by reducing or resolving uncertainty in model parameters, 3) assist the analysis to visually ensure that enough parameter samples are used to estimate the expected NMB of alternatives, and 4) are easier to interpret for decision makers compared with other methods.

Calculating the Expected Net Benefit of Sampling for Survival Data: A Tutorial and Case Study

HIGHLIGHTS

The net value of reducing decision uncertainty by collecting additional data is quantified by the expected net benefit of sampling (ENBS). This tutorial presents a general-purpose algorithm for computing the ENBS for collecting survival data along with a step-by-step implementation in R.The algorithm is based on recently published methods for simulating survival data and computing expected value of sample information that do not rely on the survival data to follow any particular parametric distribution and that can take into account any arbitrary censoring process.We demonstrate in a case study based on a previous cancer technology appraisal that ENBS calculations are useful not only for designing new studies but also for optimizing reimbursement decisions for new health technologies based on immature evidence from ongoing trials.

Rapid Assessment of the Need for Evidence: Applying the Principles of Value of Information to Research Prioritisation

We propose a short-cut heuristic approach to rapidly estimate value of information (VOI) using information commonly reported in a research funding application to make a case for the need for further evaluative research. We develop a "Rapid VOI" approach, which focuses on uncertainty in the primary outcome of clinical effectiveness and uses this to explore the health consequences of decision uncertainty. We develop a freely accessible online tool, Rapid Assessment of the Need for Evidence (RANE), to allow for the efficient computation of the value of research. As a case study, the method was applied to a proposal for research on shoulder pain rehabilitation. The analysis was included as part of a successful application for research funding to the UK National Institute for Health and Care Research. Our approach enables research funders and applicants to rapidly estimate the value of proposed research. Rapid VOI relies on information that is readily available and reported in research funding applications. Rapid VOI supports research prioritisation and commissioning decisions where there is insufficient time and resources available to develop and validate complex decision-analytic models. The method provides a practical means for implementing VOI in practice, thus providing a starting point for deliberation and contributing to the transparency and accountability of research prioritisation decisions.

Proposal for a General Outcome-Based Value Attribution Framework for Combination Therapies

OBJECTIVES

Valuing and pricing the components of combination therapies can be difficult because of competition law issues, difficulty implementing different prices for the same product in alternative uses, and attributing value to each component of the combination. We propose a value attribution solution that allows all combination components to be priced according to their relative value in the combination.

METHODS

We developed a value attribution solution that is universal, symmetrical, and neutral to each combination constituent, regardless of whether it is the backbone or the add-on, and complete, meaning that it will always attribute the full value of the combination between the component parts. Moreover, it can be applied to any number of components in the combination (eg, triplets or quadruplets). We compared this solution with 2 other existing approaches.

RESULTS

The results of the proposed value attribution solution sit between those of the 2 other value attribution approaches as it combines elements of each. As the degree of additivity moves further away from one in either direction, then our general approach ratios also move, reflecting the impact of the incremental value.

CONCLUSIONS

The proposed value attribution solution for combination therapies differs from 2 existing approaches by being universally applicable and allowing for symmetry when neutral to the constituent components of the combination. To optimally contribute to policy debate and practice, various requirements for its implementation need to be well understood, including how to overcome (1) partial information, (2) whether its assumptions can be relaxed, and (3) implementation issues.

It Takes 2 to Tango. Setting Out the Conditions in Which Performance-Based Risk-Sharing Arrangements Work for Both Parties

OBJECTIVES

Faster regulatory approval processes often fail to achieve faster patient access. We seek an approach, using performance-based risk-sharing arrangements, to address uncertainty for payers regarding the relative effectiveness and value for money of products launched through accelerated approval schemes. One important reason for risk sharing is to resolve differences of opinion between innovators and payers about a technology's underlying value. To date, there has been no formal attempt to set out the circumstances in which risk sharing can address these differences.

METHODS

We use a value of information framework to understand what a performance-based risk-sharing arrangements can, in principle, add to a reimbursement scheme, separating payer perspectives on cost-effectiveness and the value of research from those of the innovator. We find 16 scenarios, developing 5 rules to analyze these 16 scenarios, identifying cases in which risk sharing adds value for both parties.

RESULTS

We find that risk sharing provides an improved solution in 9 out of 16 combinations of payer and innovator expectations about treatment outcome and the value of further research. Among our assumptions, who pays for research and scheme administration costs are key.

CONCLUSIONS

Steps should be undertaken to make risk sharing more practical, ensuring that payers consider it an option. This requires additional costs to the health system falling on the innovator in an efficient way that aligns incentives for product development for global markets. Health systems benefits are earlier patient access to cost-effective treatments and payers with higher confidence of not wasting money. Innovators get greater returns while conducting research.

How Many Monte Carlo Samples are Needed for Probabilistic Cost-Effectiveness Analyses?

OBJECTIVES

Probabilistic sensitivity analysis (PSA) is conducted to account for the uncertainty in cost and effect of decision options under consideration. PSA involves obtaining a large sample of input parameter values (N) to estimate the expected cost and effect of each alternative in the presence of parameter uncertainty. When the analysis involves using stochastic models (eg, individual-level models), the model is further replicated P times for each sampled parameter set. We study how N and P should be determined.

METHODS

We show that PSA could be structured such that P can be an arbitrary number (say, P=1). To determine N, we derive a formula based on Chebyshev's inequality such that the error in estimating the incremental cost-effectiveness ratio (ICER) of alternatives (or equivalently, the willingness-to-pay value at which the optimal decision option changes) is within a desired level of accuracy. We described 2 methods to confirm, visually and quantitatively, that the N informed by this method results in ICER estimates within the specified level of accuracy.

RESULTS

When N is arbitrarily selected, the estimated ICERs could be substantially different from the true ICER (even as P increases), which could lead to misleading conclusions. Using a simple resource allocation model, we demonstrate that the proposed approach can minimize the potential for this error.

CONCLUSIONS

The number of parameter samples in probabilistic cost-effectiveness analyses should not be arbitrarily selected. We describe 3 methods to ensure that enough parameter samples are used in probabilistic cost-effectiveness analyses.

Making Drug Approval Decisions in the Face of Uncertainty: Cumulative Evidence versus Value of Information

BACKGROUND

The COVID-19 pandemic underscored the criticality and complexity of decision making for novel treatment approval and further research. Our study aims to assess potential decision-making methodologies, an evaluation vital for refining future public health crisis responses.

METHODS

We compared 4 decision-making approaches to drug approval and research: the Food and Drug Administration's policy decisions, cumulative meta-analysis, a prospective value-of-information (VOI) approach (using information available at the time of decision), and a reference standard (retrospective VOI analysis using information available in hindsight). Possible decisions were to reject, accept, provide emergency use authorization, or allow access to new therapies only in research settings. We used monoclonal antibodies provided to hospitalized COVID-19 patients as a case study, examining the evidence from September 2020 to December 2021 and focusing on each method's capacity to optimize health outcomes and resource allocation.

RESULTS

Our findings indicate a notable discrepancy between policy decisions and the reference standard retrospective VOI approach with expected losses up to $269 billion USD, suggesting suboptimal resource use during the wait for emergency use authorization. Relying solely on cumulative meta-analysis for decision making results in the largest expected loss, while the policy approach showed a loss up to $16 billion and the prospective VOI approach presented the least loss (up to $2 billion).

CONCLUSION

Our research suggests that incorporating VOI analysis may be particularly useful for research prioritization and treatment implementation decisions during pandemics. While the prospective VOI approach was favored in this case study, further studies should validate the ideal decision-making method across various contexts. This study's findings not only enhance our understanding of decision-making strategies during a health crisis but also provide a potential framework for future pandemic responses.

HIGHLIGHTS

This study reviews discrepancies between a reference standard (retrospective VOI, using hindsight information) and 3 conceivable real-time approaches to research-treatment decisions during a pandemic, suggesting suboptimal use of resources.Of all prospective decision-making approaches considered, VOI closely mirrored the reference standard, yielding the least expected value loss across our study timeline.This study illustrates the possible benefit of VOI results and the need for evidence accumulation accompanied by modeling in health technology assessment for emerging therapies.

Principles for Primary Care Screening in the Context of Population Health

A key component of primary care pediatrics is health promotion through screening: applying a test or procedure to detect a previously unrecognized disease or disease risk. How do we decide whether to screen? In 1965, Wilson and Jungner published an influential set of screening principles focused on the health problem's importance, the screening tool's performance, and the evidence for treatment efficacy. However, if we want realistic estimates of the population effects of routine screening, we must also account for the health care system's real-world functioning and disparities in care. We offer revised principles to guide discussions about routine screening in the primary care setting. We add to Wilson and Jungner's principles: 1. A focus on life course epidemiology and its consequences for population health, 2. A need to screen for the early stages of chronic health problems, 3. A concern for screening's acceptability to providers and the community, 4. A recommendation for estimating the uncertainty in benefits and harms in evaluating screening, 5. Inclusion of systematic plans for population data collection and monitoring, and 6. Recognition that achieving population health improvement requires a high-performing system with sufficient throughput and monitoring to deliver accessible, affordable, and effective care, especially for the groups experiencing the greatest inequities in access. Above all, instead of assuming best practices in treatment delivery and monitoring after screening, we argue for realism about the health care system functioning in routine practice.

What Is the Potential Value of a Randomized Trial of Different Thresholds to Initiate Invasive Ventilation? A Health Economic Analysis

OBJECTIVES

To estimate the expected value of undertaking a future randomized controlled trial of thresholds used to initiate invasive ventilation compared with usual care in hypoxemic respiratory failure.

PERSPECTIVE

Publicly funded healthcare payer.

SETTING

Critical care units capable of providing invasive ventilation and unconstrained by resource limitations during usual (nonpandemic) practice.

METHODS

We performed a model-based cost-utility estimation with individual-level simulation and value-of-information analysis focused on adults, admitted to critical care, receiving noninvasive oxygen. In the primary scenario, we compared hypothetical threshold A to usual care, where threshold A resulted in increased use of invasive ventilation and improved survival compared with usual care. In the secondary scenario, we compared hypothetical threshold B to usual care, where threshold B resulted in decreased use of invasive ventilation and similar survival compared with usual care. We assumed a willingness-to-pay of 100,000 Canadian dollars (CADs) per quality-adjusted life year.

RESULTS

In the primary scenario, threshold A was cost-effective compared with usual care due to improved hospital survival (78.1% vs. 75.1%), despite more use of invasive ventilation (62% vs. 30%) and higher lifetime costs (86,900 vs. 75,500 CAD). In the secondary scenario, threshold B was cost-effective compared with usual care due to similar survival (74.5% vs. 74.6%) with less use of invasive ventilation (20.2% vs. 27.6%) and lower lifetime costs (71,700 vs. 74,700 CAD). Value-of-information analysis showed that the expected value to Canadian society over 10 years of a 400-person randomized trial comparing a threshold for invasive ventilation to usual care in hypoxemic respiratory failure was 1.35 billion CAD or more in both scenarios.

CONCLUSIONS

It would be highly valuable to society to identify thresholds that, in comparison to usual care, either increase survival or reduce invasive ventilation without reducing survival.

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.

Exploring Structural Uncertainty in Cost-Effectiveness Modeling of Gestational Diabetes Screening: An Application Example from Norway

BACKGROUND

Screening pregnant women for gestational diabetes mellitus (GDM) has recently been expanded in Norway, although screening eligibility criteria continue to be debated. We aimed to compare the cost-effectiveness of alternative GDM screening strategies and explored structural uncertainty and the value of future research in determining the most cost-effective eligibility criteria for GDM screening in Norway.

DESIGN

We developed a probabilistic decision tree to estimate the total costs and health benefits (i.e., quality-adjusted life-years; QALYs) associated with 4 GDM screening strategies (universal, current guidelines, high-risk, and no screening). We identified the most cost-effective strategy as the strategy with the highest incremental cost-effectiveness ratio below a Norwegian benchmark for cost-effectiveness ($28,400/QALY). We excluded inconclusive evidence on the effects of screening on later maternal type 2 diabetes mellitus (T2DM) in the primary analysis but included this outcome in a secondary analysis using 2 different sources of evidence (i.e., Cochrane or US Preventive Services Task Force). To quantify decision uncertainty, we conducted scenario analysis and value-of-information analyses.

RESULTS

Current screening recommendations were considered inefficient in all analyses, while universal screening was most cost-effective in our primary analysis ($26,014/QALY gained) and remained most cost-effective when we assumed a preventive effect of GDM treatment on T2DM. When we assumed no preventive effect, high-risk screening was preferred ($19,115/QALY gained). When we assumed GDM screening does not prevent perinatal death in scenario analysis, all strategies except no screening exceeded the cost-effectiveness benchmark. In most analyses, decision uncertainty was high.

CONCLUSIONS

The most cost-effective screening strategy, ranging from no screening to universal screening, depended on the source and inclusion of GDM treatment effects on perinatal death and T2DM. Further research on these long-term outcomes could reduce decision uncertainty.

HIGHLIGHTS

This article analyses the cost-effectiveness of 4 alternative gestational diabetes mellitus (GDM) screening strategies in Norway: universal screening, current (broad) screening, high-risk screening, and no screening.The current Norwegian screening recommendations were considered inefficient under all analyses.The most cost-effective screening strategy ranged from no screening to universal screening depending on the source and inclusion of GDM treatment effects on later maternal diabetes and perinatal death.The parameters related to later maternal diabetes and perinatal death accounted for most of the decision uncertainty.

A Guide to an Iterative Approach to Model-Based Decision Making in Health and Medicine: An Iterative Decision-Making Framework

Decision makers frequently face decisions about optimal resource allocation. A model-based economic evaluation can be used to guide decision makers in their choices by systematically evaluating the magnitude of expected health effects and costs of decision options and by making trade-offs explicit. We provide a guide to an iterative approach to the medical decision-making process by following a coherent framework, and outline the overarching iterative steps of model-based decision making. We systematized the framework by performing three steps. First, we compiled the existing guidelines provided by the ISPOR-SMDM Modeling Good Research Practices Task Force, and the ISPOR Value of Information Task Force. Second, we identified other previous work related to frameworks and guidelines for model-based decision analyses through a literature search in PubMed. Third, we assessed the role of the evidence and iterative process in decision making and formalized key steps in a model-based decision-making framework. We provide guidance on an iterative approach to medical decision making by applying the compiled iterative model-based decision-making framework. The framework formally combines the decision problem conceptualization (Part I), the model conceptualization and development (Part II), and the process of model-based decision analysis (Part III). Following the overarching steps of the framework ensures compliance to the principles of evidence-based medicine and regular updates of the evidence, given that value of information analysis represents an essential component of model-based decision analysis in the framework. Following the provided guide and the steps outlined in the framework can help inform various health care decisions, and therefore it has the potential to improve decision making.

Digital Alcohol Interventions Could Be Part of the Societal Response to Harmful Consumption, but We Know Little About Their Long-Term Costs and Health Outcomes

Alcohol consumption causes both physical and psychological harm and is a leading risk factor for noncommunicable diseases. Digital alcohol interventions have been found to support those looking for help by giving them tools for change. However, whether digital interventions can help tackle the long-term societal consequences of harmful alcohol consumption in a cost-effective manner has not been adequately evaluated. In this Viewpoint, we propose that studies of digital alcohol interventions rarely evaluate the consequences of wider dissemination of the intervention under study, and that when they do, they do not take advantage of modeling techniques that allow for appropriately studying consequences over a longer time horizon than the study period when the intervention is tested. We argue that to help decision-makers to prioritize resources for research and dissemination, it is important to model long-term costs and health outcomes. Further, this type of modeling gives important insights into the context in which interventions are studied and highlights where more research is required and where sufficient evidence is available. The viewpoint therefore invites the researcher not only to reflect on which interventions to study but also how to evaluate their long-term consequences.

Identifying the Optimum Strategy for Identifying Adults and Children With Celiac Disease: A Cost-Effectiveness and Value of Information Analysis

OBJECTIVES

Celiac disease (CD) is thought to affect around 1% of people in the United Kingdom, but only approximately 30% are diagnosed. The aim of this work was to assess the cost-effectiveness of strategies for identifying adults and children with CD in terms of who to test and which tests to use.

METHODS

A decision tree and Markov model were used to describe testing strategies and model long-term consequences of CD. The analysis compared a selection of pre-test probabilities of CD above which patients should be screened, as well as the use of different serological tests, with or without genetic testing. Value of information analysis was used to prioritize parameters for future research.

RESULTS

Using serological testing alone in adults, immunoglobulin A (IgA) tissue transglutaminase (tTG) at a 1% pre-test probability (equivalent to population screening) was most cost-effective. If combining serological testing with genetic testing, human leukocyte antigen combined with IgA tTG at a 5% pre-test probability was most cost-effective. In children, the most cost-effective strategy was a 10% pre-test probability with human leukocyte antigen plus IgA tTG. Value of information analysis highlighted the probability of late diagnosis of CD and the accuracy of serological tests as important parameters. The analysis also suggested prioritizing research in adult women over adult men or children.

CONCLUSIONS

For adults, these cost-effectiveness results suggest UK National Screening Committee Criteria for population-based screening for CD should be explored. Substantial uncertainty in the results indicate a high value in conducting further research.

Value of Information Analysis of a Web-Based Self-Management Intervention for Chronic Obstructive Pulmonary Disease

Objective: Technology-based programs can be cost-effective in the management of chronic obstructive pulmonary disease (COPD). However, cost-effectiveness estimates always contain some uncertainty, and decisions based upon them carry some risk. We conducted a value of information (VOI) analysis to estimate the value of additional research of a web-based self-management intervention for COPD to reduce the costs associated with uncertainty. Methods: We used a 10,000-iteration cost-effectiveness model from the health care payer perspective to calculate the expected value of perfect information (EVPI) at the patient- and population-level. An opportunity loss was incurred when the web-based intervention did not produce a greater net monetary benefit than usual care in an iteration. We calculated the probability of opportunity loss and magnitude of opportunity costs as a function of baseline health utility. We aggregated opportunity costs over the projected incident population of inpatient COPD patients over 10 years and estimated it as a function of the willingness-to-pay (WTP) threshold. Costs are in 2022 U.S. Dollars. Results: Opportunity losses were found in 22.7% of the iterations. The EVPIpatient was $78 per patient (95% confidence interval: $75-$82). The probability that the intervention was the optimal strategy varied across baseline health utilities. The EVPIpopulation was $506,666,882 over 10 years for a WTP of $50,000. Conclusions: Research estimated to cost up to $500 million would be warranted to reduce uncertainty. Future research could focus on identifying the impact of baseline health utilities to maximize the cost savings of the intervention. Other considerations for future research priorities include implementation efforts for technology-based interventions.

Early Cost-Effectiveness Analysis of Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy for Gastric Cancer Patients with Limited Peritoneal Carcinomatosis

BACKGROUND

Gastric cancer patients with peritoneal carcinomatosis (PC) have a poor prognosis, with a median overall survival of 10 months when treated with systemic chemotherapy only. Cohort studies showed that cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) might improve the prognosis for gastric cancer patients with limited PC. Besides generating trial data on clinical effectiveness, it is crucial to timely collect information on economic aspects to guide the reimbursement decision-making process. No previous data have been published on the cost(-effectiveness) of CRS/HIPEC in this group of patients. Therefore, we performed an early model-based cost-effectiveness analysis of CRS/HIPEC for gastric cancer patients with limited PC in the Dutch setting.

METHODS

We constructed a two-state (alive-dead) Markov transition model to evaluate costs and clinical outcomes from a Dutch healthcare perspective. Clinical outcomes, transition probabilities and utilities were derived from literature and verified by clinical experts in the field. Costs were measured using two available representative cohorts (2010-2017): one 'systemic chemotherapy only' cohort and one 'CRS/HIPEC' cohort (n = 10 each). Incremental cost-utility ratios (ICURs) were expressed as Euros per quality-adjusted life-year (QALY). We performed probabilistic and deterministic sensitivity, scenario, and value-of-information analyses using a willingness-to-pay (WTP) threshold of €80,000/QALY, which reflects the Dutch norm for severe diseases.

RESULTS

In the base-case analysis, CRS/HIPEC yielded more QALYs (increment of 0.68) and more costs (increment of €34,706) compared with systemic chemotherapy only, resulting in an ICUR of €50,990/QALY. The probability that CRS/HIPEC was cost effective compared with systemic chemotherapy alone was 64%. To reduce uncertainty, the expected value of perfect information amounted to €4,021,468. The scenario analyses did not alter the results and showed that treatment costs, lifetime health-related quality of life and overall survival had the largest influence on the model.

CONCLUSIONS

The presented early cost-effectiveness analysis suggests that adding CRS/HIPEC to systemic chemotherapy for gastric cancer patients with limited PC has a good chance of being cost-effectiveness compared with systemic chemotherapy alone when using a WTP of €80,000/QALY. However, there is substantial uncertainty in view of the current available data on effectiveness. Results from the ongoing phase III PERISCOPE II trial are therefore crucial for further decisions on treatment policy and its cost-effectiveness.

The economic burden of Ebola virus disease: a review and recommendations for analysis

BACKGROUND

Ebola virus disease (EVD) continues to be a major public health threat globally, particularly in the low-and-middle-income countries (LMICs) of Africa. The social and economic burdens of EVD are substantial and have triggered extensive research into prevention and control. We aim to highlight the impact and economic implications, identify research gaps, and offer recommendations for future economic studies pertaining to EVD.

METHOD

We conducted a comprehensive librarian-led search in PubMed/Medline, Embase, Google Scholar, EconLit and Scopus for economic evaluations of EVD. After study selection and data extraction, findings on the impact and economics of EVD were synthesized using a narrative approach, while identifying gaps, and recommending critical areas for future EVD economic studies.

RESULTS

The economic evaluations focused on the burden of illness, vaccine cost-effectiveness, willingness-to-pay for a vaccine, EVD funding, and preparedness costs. The estimated economic impact of the 2014 EVD outbreak in Guinea, Liberia, and Sierra Leone across studies ranged from $30 billion to $50 billion. Facility construction and modification emerged as significant cost drivers for preparedness. The EVD vaccine demonstrated cost-effectiveness in a dynamic transmission model; resulting in an incremental cost-effectiveness ratio of about $96 per additional disability adjusted life year averted. Individuals exhibited greater willingness to be vaccinated if it incurred no personal cost, with a minority willing to pay about $1 for the vaccine.

CONCLUSIONS

The severe impact of EVD puts pressure on governments and the international community for better resource utilization and re-allocation. Several technical and methodological issues related to economic evaluation of EVD remain to be addressed, especially for LMICs. We recommend conducting cost-of-sequelae and cost-of-distribution analyses in addition to adapting existing economic analytical methods to EVD. Characteristics of the affected regions should be considered to provide evidence-based economic plans and economic-evaluation of mitigations that enhance resource allocation for prevention and treatment.

Estimating the Potential Benefits of Confirmatory Trials for Drugs with Accelerated Approval: A Comprehensive Value of Information Framework

BACKGROUND

The US Food and Drug Administration's Accelerated Approval (AA) policy provides a pathway for patients to access potentially life-saving drugs rapidly. However, the use of surrogate endpoints, single-arm designs, and small sample sizes in preliminary trials that support AAs can lead to uncertainty regarding the clinical benefits of such drugs. This study aims to develop a comprehensive value of information (VOI) framework for assessing the potential benefits of future confirmatory trials, accounting for the various uncertainties inherent in preliminary trials.

METHODS

I formulated an expected value of information from confirmatory trial (EVICT) metric, which evaluates the potential benefits of a confirmatory trial that would reduce those uncertainties by using a clinically meaningful endpoint, a randomized control, and increased sample size. The EVICT metric can quantify the expected benefits of a well-designed confirmatory trial or an inadequately designed one that continues to use surrogate endpoints or single-arm design. The framework was illustrated using a hypothetical AA drug for metastatic breast cancer.

RESULTS

The case study demonstrates that a highly uncertain preliminary trial of an AA drug was associated with a substantial EVICT. A confirmatory trial with an increased sample size for this AA drug, utilizing a clinically meaningful endpoint and randomized control, yielded a population-level EVICT of $12.6 million. Persistently using a surrogate endpoint and single-arm trial design would reduce the EVICT by 60%.

CONCLUSIONS

This framework can provide accurate VOI estimates to guide coverage policies, value-based pricing, and the design of confirmatory trials for AA drugs.

Consolidated Health Economic Evaluation Reporting Standards - Value of Information (CHEERS-VOI): Explanation and Elaboration

OBJECTIVES

Although the ISPOR Value of Information (VOI) Task Force's reports outline VOI concepts and provide good-practice recommendations, there is no guidance for reporting VOI analyses. VOI analyses are usually performed alongside economic evaluations for which the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) 2022 Statement provides reporting guidelines. Thus, we developed the CHEERS-VOI checklist to provide reporting guidance and checklist to support the transparent, reproducible, and high-quality reporting of VOI analyses.

METHODS

A comprehensive literature review generated a list of 26 candidate reporting items. These candidate items underwent a Delphi procedure with Delphi participants through 3 survey rounds. Participants rated each item on a 9-point Likert scale to indicate its relevance when reporting the minimal, essential information about VOI methods and provided comments. The Delphi results were reviewed at 2-day consensus meetings and the checklist was finalized using anonymous voting.

RESULTS

We had 30, 25, and 24 Delphi respondents in rounds 1, 2, and 3, respectively. After incorporating revisions recommended by the Delphi participants, all 26 candidate items proceeded to the 2-day consensus meetings. The final CHEERS-VOI checklist includes all CHEERS items, but 7 items require elaboration when reporting VOI. Further, 6 new items were added to report information relevant only to VOI (eg, VOI methods applied).

CONCLUSIONS

The CHEERS-VOI checklist should be used when a VOI analysis is performed alongside economic evaluations. The CHEERS-VOI checklist will help decision makers, analysts and peer reviewers in the assessment and interpretation of VOI analyses and thereby increase transparency and rigor in decision making.

Alternative cascade-testing protocols for identifying and managing patients with familial hypercholesterolaemia: systematic reviews, qualitative study and cost-effectiveness analysis

Background

Cascade testing the relatives of people with familial hypercholesterolaemia is an efficient approach to identifying familial hypercholesterolaemia. The cascade-testing protocol starts with identifying an index patient with familial hypercholesterolaemia, followed by one of three approaches to contact other relatives: indirect approach, whereby index patients contact their relatives; direct approach, whereby the specialist contacts the relatives; or a combination of both direct and indirect approaches. However, it is unclear which protocol may be most effective.

Objectives

The objectives were to determine the yield of cases from different cascade-testing protocols, treatment patterns, and short- and long-term outcomes for people with familial hypercholesterolaemia; to evaluate the cost-effectiveness of alternative protocols for familial hypercholesterolaemia cascade testing; and to qualitatively assess the acceptability of different cascade-testing protocols to individuals and families with familial hypercholesterolaemia, and to health-care providers.

Design and methods

This study comprised systematic reviews and analysis of three data sets: PASS (PASS Software, Rijswijk, the Netherlands) hospital familial hypercholesterolaemia databases, the Clinical Practice Research Datalink (CPRD)-Hospital Episode Statistics (HES) linked primary-secondary care data set, and a specialist familial hypercholesterolaemia register. Cost-effectiveness modelling, incorporating preceding analyses, was undertaken. Acceptability was examined in interviews with patients, relatives and health-care professionals.

Result

Systematic review of protocols: based on data from 4 of the 24 studies, the combined approach led to a slightly higher yield of relatives tested [40%, 95% confidence interval (CI) 37% to 42%] than the direct (33%, 95% CI 28% to 39%) or indirect approaches alone (34%, 95% CI 30% to 37%). The PASS databases identified that those contacted directly were more likely to complete cascade testing (p < 0.01); the CPRD-HES data set indicated that 70% did not achieve target treatment levels, and demonstrated increased cardiovascular disease risk among these individuals, compared with controls (hazard ratio 9.14, 95% CI 8.55 to 9.76). The specialist familial hypercholesterolaemia register confirmed excessive cardiovascular morbidity (standardised morbidity ratio 7.17, 95% CI 6.79 to 7.56). Cost-effectiveness modelling found a net health gain from diagnosis of -0.27 to 2.51 quality-adjusted life-years at the willingness-to-pay threshold of £15,000 per quality-adjusted life-year gained. The cost-effective protocols cascaded from genetically confirmed index cases by contacting first- and second-degree relatives simultaneously and directly. Interviews found a service-led direct-contact approach was more reliable, but combining direct and indirect approaches, guided by index patients and family relationships, may be more acceptable.

Limitations

Systematic reviews were not used in the economic analysis, as relevant studies were lacking or of poor quality. As only a proportion of those with primary care-coded familial hypercholesterolaemia are likely to actually have familial hypercholesterolaemia, CPRD analyses are likely to underestimate the true effect. The cost-effectiveness analysis required assumptions related to the long-term cardiovascular disease risk, the effect of treatment on cholesterol and the generalisability of estimates from the data sets. Interview recruitment was limited to white English-speaking participants.

Conclusions

Based on limited evidence, most cost-effective cascade-testing protocols, diagnosing most relatives, select index cases by genetic testing, with services directly contacting relatives, and contacting second-degree relatives even if first-degree relatives have not been tested. Combined approaches to contact relatives may be more suitable for some families.

Future work

Establish a long-term familial hypercholesterolaemia cohort, measuring cholesterol levels, treatment and cardiovascular outcomes. Conduct a randomised study comparing different approaches to contact relatives.

Study registration

This study is registered as PROSPERO CRD42018117445 and CRD42019125775.

Funding

This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 27, No. 16. See the NIHR Journals Library website for further project information.

Evaluating high-cost technologies - no need to throw the baby out with the bathwater

INTRODUCTION

Evidence generation for the health technology assessment (HTA) of a new technology is a long and expensive process with no guarantees that the health technology will be adopted and implemented into a health-care system. This would suggest that there is a greater risk of failure for a company developing a high-cost technology and therefore incentives (such as increasing the funding available for research or additional market exclusivity) may be needed to encourage development of such technologies as has been seen with many high-cost orphan drugs.

AREAS COVERED

This paper discusses some of the key issues relating to the evaluation of high-cost technologies through the use of existing HTA processes and what the challenges will be going forward.

EXPERT OPINION

We propose that while the current HTA process is robust, its evolution into accommodating the incorporation of real-world data and evidence alongside a life-cycle HTA approach should better enable developers to produce the evidence required on effectiveness and cost-effectiveness. This should lead to reduced decision uncertainty for HTA agencies to make adoption decisions in a more timely and efficient manner. Furthermore, budget impact analysis remains important in understanding the actual financial impact on health-care systems and budgets outside of the cost-effectiveness framework used to aid decision-making.

Economic evaluation of pharmacogenomic-guided antiplatelet treatment in Spanish patients suffering from acute coronary syndrome participating in the U-PGx PREPARE study

BACKGROUND

Cardiovascular diseases and especially Acute Coronary Syndrome (ACS) constitute a major health issue impacting millions of patients worldwide. Being a leading cause of death and hospital admissions in many European countries including Spain, it accounts for enormous amounts of healthcare expenditures for its management. Clopidogrel is one of the oldest antiplatelet medications used as standard of care in ACS.

METHODS

In this study, we performed an economic evaluation study to estimate whether a genome-guided clopidogrel treatment is cost-effective compared to conventional one in a large cohort of 243 individuals of Spanish origin suffering from ACS and treated with clopidogrel. Data were derived from the U-PGx PREPARE clinical trial. Effectiveness was measured as survival of individuals while study data on safety and efficacy, as well as on resource utilization associated with each adverse drug reaction were used to measure costs to treat these adverse drug reactions. A generalized linear regression model was used to estimate cost differences for both study groups.

RESULTS

Based on our findings, PGx-guided treatment group is cost-effective. PGx-guided treatment demonstrated to have 50% less hospital admissions, reduced emergency visits and almost 13% less ADRs compared to the non-PGx approach with mean QALY 1.07 (95% CI, 1.04-1.10) versus 1.06 (95% CI, 1.03-1.09) for the control group, while life years for both groups were 1.24 (95% CI, 1.20-1.26) and 1.23 (95% CI, 1.19-1.26), respectively. The mean total cost of PGx-guided treatment was 50% less expensive than conventional therapy with clopidogrel [€883 (95% UI, €316-€1582), compared to €1,755 (95% UI, €765-€2949)].

CONCLUSION

These findings suggest that PGx-guided clopidogrel treatment represents a cost-effective option for patients suffering from ACS in the Spanish healthcare setting.

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.

Use of Decision-Analytic Modelling to Assess the Cost-Effectiveness of Diagnostic Imaging of the Spine, Shoulder, and Knee: A Scoping Review

BACKGROUND

Limited evidence is available on the cost-effectiveness of diagnostic imaging for back, neck, knee, and shoulder complaints. Decision analytic modelling may be an appropriate method to synthesise evidence from multiple sources, and overcomes issues with trial-based economic evaluations.

OBJECTIVE

The aim was to describe the reporting of methods and objectives utilised in existing decision analytic modelling studies that assess the cost-effectiveness of diagnostic imaging for back, neck, knee, and shoulder complaints.

METHODS

Decision analytic modelling studies investigating the use of any imaging modality for people of any age with back, neck, knee, or shoulder complaints were included. No restrictions on comparators were applied, and included studies were required to estimate both costs and benefits. A systematic search (5 January 2023) of four databases was conducted with no date limits imposed. Methodological and knowledge gaps were identified through a narrative summary.

RESULTS

Eighteen studies were included. Methodological issues were identified relating to the poor reporting of methods, and measures of effectiveness did not incorporate changes in quantity and/or quality of life (cost-utility analysis in only ten of 18 studies). Included studies, particularly those investigating back or neck complaints, focused on conditions that were of low prevalence but have a serious impact on health (i.e. cervical spine trauma, cancer-related back pain).

CONCLUSIONS

Future models should pay particular attention to the identified methodological and knowledge gaps. Investment in the health technology assessment of these commonly utilised diagnostic imaging services is needed to justify the current level of utilisation and ensure that these services represent value for money.

Protocol for a bandit-based response adaptive trial to evaluate the effectiveness of brief self-guided digital interventions for reducing psychological distress in university students: the Vibe Up study

INTRODUCTION

Meta-analytical evidence confirms a range of interventions, including mindfulness, physical activity and sleep hygiene, can reduce psychological distress in university students. However, it is unclear which intervention is most effective. Artificial intelligence (AI)-driven adaptive trials may be an efficient method to determine what works best and for whom. The primary purpose of the study is to rank the effectiveness of mindfulness, physical activity, sleep hygiene and an active control on reducing distress, using a multiarm contextual bandit-based AI-adaptive trial method. Furthermore, the study will explore which interventions have the largest effect for students with different levels of baseline distress severity.

METHODS AND ANALYSIS

The Vibe Up study is a pragmatically oriented, decentralised AI-adaptive group sequential randomised controlled trial comparing the effectiveness of one of three brief, 2-week digital self-guided interventions (mindfulness, physical activity or sleep hygiene) or active control (ecological momentary assessment) in reducing self-reported psychological distress in Australian university students. The adaptive trial methodology involves up to 12 sequential mini-trials that allow for the optimisation of allocation ratios. The primary outcome is change in psychological distress (Depression, Anxiety and Stress Scale, 21-item version, DASS-21 total score) from preintervention to postintervention. Secondary outcomes include change in physical activity, sleep quality and mindfulness from preintervention to postintervention. Planned contrasts will compare the four groups (ie, the three intervention and control) using self-reported psychological distress at prespecified time points for interim analyses. The study aims to determine the best performing intervention, as well as ranking of other interventions.

ETHICS AND DISSEMINATION

Ethical approval was sought and obtained from the UNSW Sydney Human Research Ethics Committee (HREC A, HC200466). A trial protocol adhering to the requirements of the Guideline for Good Clinical Practice was prepared for and approved by the Sponsor, UNSW Sydney (Protocol number: HC200466_CTP).

TRIAL REGISTRATION NUMBER

ACTRN12621001223820.

Cost-effectiveness of Dental Workforce Expansion Through the National Health Service Corps and Its Association With Oral Health Outcomes Among US Children

Importance

Despite considerable efforts to improve oral health for all, large disparities remain among US children. A dental professional shortage is thought to be among the determinants associated with oral health disparities, particularly for those residing in underserved communities.

Objective

To evaluate the cost-effectiveness of expanding the dental workforce through the National Health Service Corps (NHSC) and associations with oral health outcomes among US children.

Design, Setting, and Participants

A cost-effectiveness analysis was conducted to estimate changes in total costs and quality-adjusted life years (QALYs) produced by increasing the NHSC funding for dental practitioners by 5% to 30% during a 10-year period. A microsimulation model of oral health outcomes using a decision analytic framework was constructed based on oral health and dental care utilization data of US children from 0 to 19 years old. Data from the nationally representative National Health and Nutrition Examination Survey (NHANES, 2011-2016) were linked to county-level dentist supply and oral health professional shortage areas (HPSAs) information. Changes in prevalence and cumulative incidence of dental caries were also estimated. Sensitivity analyses were conducted to assess the robustness of results to variation in model input parameters. Data analysis was conducted from August 1, 2021, to November 1, 2022.

Exposures

Expanding dental workforce through the NHSC program.

Main Outcomes and Measures

Changes in total QALYs, costs, and dental caries prevalence and cumulative incidence.

Results

This simulation model informed by NHANES data of 10 780 participants (mean [SD] age, 9.6 [0.1] years; 5326 [48.8%] female; 3337 [weighted percentage, 57.9%] non-Hispanic White individuals) found that when funding for the NHSC program increased by 10%, dental caries prevalence and total number of decayed teeth were estimated to decrease by 0.91 (95% CI, 0.82-1.00) percentage points and by 0.70 (95% CI, 0.62-0.79) million cases, respectively. When funding for the NHSC program increased between 5% and 30%, the estimated decreases in number of decayed teeth ranged from 0.35 (95% CI, 0.27-0.44) to 2.11 (95% CI, 2.03-2.20) million cases, total QALY gains ranged from 75.76 (95% CI, 59.44-92.08) to 450.50 (95% CI, 434.30-466.69) thousand QALYs, and total cost savings ranged from $105.53 (95% CI, $70.14-$140.83) to $508.23 (95% CI, $598.91-$669.22) million among children residing in dental HPSAs from a health care perspective. Benefits of the intervention accrued most substantially among Hispanic children and children in low-income households.

Conclusions and Relevance

This cost-effectiveness analysis using a decision analytic model suggests that expanding the dental workforce through the NHSC program would be associated with cost savings and a reduced risk of dental caries among children living in HPSAs.

Information Density in Decision Analysis

Information value has been proposed and used as a probabilistic sensitivity measure, the idea being that uncertain parameters having higher information value are precisely those to which an optimal decision is more sensitive. In this paper, we study the notion of information density as a graphical complement to information value analysis, one that augments an information value calculation with associated directions of information gain. We formally examine mathematical details absent from its earlier presentation that guarantee information density exists and is well posed and describe its relationship to alternate measures of information value. We present its application in the context of a realistic case study and discuss the associated insights.

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.

Economics and Precision Medicine

Introducing precision medicine strategies into routine practice will require robust economic evidence. Decision-makers need to understand the value of a precision medicine strategy compared with alternative ways to treat patients. This chapter describes health economic analysis techniques that are needed to generate this evidence. The value of any precision medicine strategy can be demonstrated early to inform evidence generation and improve the likelihood of translation into routine practice. Advances in health economic analysis techniques are also explained and their relevance to precision medicine is highlighted. Ensuring that constraints on delivery are resolved to increase uptake and implementation will improve the value of a new precision medicine strategy. Empirical methods to quantify stakeholders' preferences can be effective to inform the design of a precision medicine intervention or service delivery model. A range of techniques to generate relevant economic evidence are now available to support the development and translation of precision medicine into routine practice. This economic evidence is essential to inform resource allocation decisions and will enable patients to benefit from cost-effective precision medicine strategies in the future.

Resolving sex and gender bias in COVID-19 vaccines R&D and beyond

The influence of sex and gender in immune response and vaccine outcomes is established in many disease areas, including in COVID-19. Yet, there are notable gaps in the consideration of sex and gender in the analysis and reporting of COVID-19 vaccines clinical trial data. The push for stronger sex and gender integration in vaccines science should be championed by all researchers and stakeholders across the R&D and access ecosystem - not just gender experts. This requires joint action on the tactical framing of customized value propositions (based on stakeholder motivations), the stronger enforcement of existing regulation, tools, and commitments, and aligning the overall agenda to parallel calls on intersectionality, equity diversity and inclusion.

Approaches to prioritising research for clinical trial networks: a scoping review

BACKGROUND

Prioritisation of clinical trials ensures that the research conducted meets the needs of stakeholders, makes the best use of resources and avoids duplication. The aim of this review was to identify and critically appraise approaches to research prioritisation applicable to clinical trials, to inform best practice guidelines for clinical trial networks and funders.

METHODS

A scoping review of English-language published literature and research organisation websites (January 2000 to January 2020) was undertaken to identify primary studies, approaches and criteria for research prioritisation. Data were extracted and tabulated, and a narrative synthesis was employed.

RESULTS

Seventy-eight primary studies and 18 websites were included. The majority of research prioritisation occurred in oncology and neurology disciplines. The main reasons for prioritisation were to address a knowledge gap (51 of 78 studies [65%]) and to define patient-important topics (28 studies, [35%]). In addition, research organisations prioritised in order to support their institution's mission, invest strategically, and identify best return on investment. Fifty-seven of 78 (73%) studies used interpretative prioritisation approaches (including Delphi surveys, James Lind Alliance and consensus workshops); six studies used quantitative approaches (8%) such as prospective payback or value of information (VOI) analyses; and 14 studies used blended approaches (18%) such as nominal group technique and Child Health Nutritional Research Initiative. Main criteria for prioritisation included relevance, appropriateness, significance, feasibility and cost-effectiveness.

CONCLUSION

Current research prioritisation approaches for groups conducting and funding clinical trials are largely interpretative. There is an opportunity to improve the transparency of prioritisation through the inclusion of quantitative approaches.

Mathematical Models for Evaluating Effectiveness and Cost-Effectiveness of Cervical Cancer Control Policies in Populations Including Women Living With Human Immunodeficiency Virus: A Scoping Review

OBJECTIVES

Mathematical modeling is increasingly used to inform cervical cancer control policies, and model-based evaluations of such policies in women living with human immunodeficiency virus (HIV) are an emerging research area. We did a scoping review of published literature to identify research gaps and inform future work in this field.

METHODS

We systematically searched literature up to April 2022 and included mathematical modeling studies evaluating the effectiveness or cost-effectiveness of cervical cancer prevention strategies in populations including women living with HIV. We extracted information on prevention strategies and modeling approaches.

RESULTS

We screened 1504 records and included 22 studies, almost half of which focused on South Africa. We found substantial between-study heterogeneity in terms of strategies assessed and modeling approaches used. Fourteen studies evaluated cervical cancer screening strategies, 7 studies assessed human papillomavirus vaccination (with or without screening), and 1 study evaluated the impact of HIV control measures on cervical cancer incidence and mortality. Thirteen conducted cost-effectiveness analyses. Markov cohort state-transition models were used most commonly (n = 12). Most studies (n = 17) modeled the effect of HIV by creating HIV-related health states. Thirteen studies performed model calibration, but 11 did not report the calibration methods used. Only 1 study stated that model code was available upon request.

CONCLUSIONS

Few model-based evaluations of cervical cancer control strategies have specifically considered women living with HIV. Improvements in model transparency, by sharing information and making model code publicly available, could facilitate the utility of these evaluations for other high disease-burden countries, where they are needed for assisting policy makers.

Why We Need a New Outcomes-Based Value Attribution Framework for Combination Regimens in Oncology

BACKGROUND

Novel oncology treatment strategies increasingly use medicines with distinct but complementary mechanisms of action in combination or in close sequence. Payers, when confronted with higher total cost of providing combination regimens involving multiple therapies and usually longer treatment durations, are reluctant to reimburse them, particularly when they perceive the expected incremental benefits from adding a new medicine (the add-on) to a currently reimbursed medicine (the backbone) not to represent value for money to the health system. Nevertheless, depending on how value is attributed to the add-on versus the backbone, a clinically effective medicine used as part of a regimen that increases treatment duration might be found "not cost-effective at zero price." This phenomenon, signaling a policy problem not a pricing issue, first needs to be better understood before a generalizable and transparent solution can be presented.

OBJECTIVE

This article sets out when this policy challenge arises and describes general principles that any proposed solution to the value attribution problem must satisfy.

METHODS

We develop a simplified conceptual framework and use this to address 2 topics. The first is to understand the origin of problems posed by the current approach for attributing value in incremental cost-effectiveness analyses of combination regimens. The second is to discuss 2 new approaches in the literature designed to address the challenge.

FINDINGS

We find that neither meets our criteria, meaning that further work is needed to resolve the issue. Finally, we briefly discuss the implications of relaxing the simplifying assumptions in our conceptual framework.

Assessing the potential of HTA to inform resource allocation decisions in low-income settings: The case of Malawi

Health technology assessment (HTA) offers a set of analytical tools to support health systems' decisions about resource allocation. Although there is increasing interest in these tools across the world, including in some middle-income countries, they remain rarely used in low-income countries (LICs). In general, the focus of HTA is narrow, mostly limited to assessments of efficacy and cost-effectiveness. However, the principles of HTA can be used to support a broader series of decisions regarding new health technologies. We examine the potential for this broad use of HTA in LICs, with a focus on Malawi. We develop a framework to classify the main decisions on health technologies within health systems. The framework covers decisions on identifying and prioritizing technologies for detailed assessment, deciding whether to adopt an intervention, assessing alternative investments for implementation and scale-up, and undertaking further research activities. We consider the relevance of the framework to policymakers in Malawi and we use two health technologies as examples to investigate the main barriers and enablers to the use of HTA methods. Although the scarcity of local data, expertise, and other resources could risk limiting the operationalisation of HTA in LICs, we argue that even in highly resource constrained health systems, such as in Malawi, the use of HTA to support a broad range of decisions is feasible and desirable.

Adapting population health interventions for new contexts: qualitative interviews understanding the experiences, practices and challenges of researchers, funders and journal editors

OBJECTIVES

Research on the adaptation of population health interventions for implementation in new contexts is rapidly expanding. This has been accompanied by a recent increase in the number of frameworks and guidance to support adaptation processes. Nevertheless, there remains limited exploration of the real-world experiences of undertaking intervention adaptation, notably the challenges encountered by different groups of stakeholders, and how these are managed. Understanding experiences is imperative in ensuring that guidance to support adaptation has practical utility. This qualitative study examines researcher and stakeholder experiences of funding, conducting and reporting adaptation research.

SETTING

Adaptation studies.

PARTICIPANTS

Participants/cases were purposefully sampled to represent a range of adapted interventions, types of evaluations, expertise and countries. Semistructured interviews were conducted with a sample of researchers (n=23), representatives from research funding panels (n=6), journal editors (n=5) and practitioners (n=3).

MEASURES

A case study research design was used. Data were analysed using the framework approach. Overarching themes were discussed within the study team, with further iterative refinement of subthemes.

RESULTS

The results generated four central themes. The first three relate to the experience of intervention adaptation (1) involving stakeholders throughout the adaptation process and how to integrate the evidence base with experience; (2) selecting the intervention and negotiating the mismatch between the original and the new context; and (3) the complexity and uncertainty when deciding the re-evaluation process. The final theme (4) reflects on participants' experiences of using adaptation frameworks in practice, considering recommendations for future guidance development and refinement.

CONCLUSION

This study highlights the range of complexities and challenges experienced in funding, conducting and reporting research on intervention adaptation. Moving forward, guidance can be helpful in systematising processes, provided that it remains responsive to local contexts and encourage innovative practice.

Defining the optimum strategy for identifying adults and children with coeliac disease: systematic review and economic modelling

BACKGROUND

Coeliac disease is an autoimmune disorder triggered by ingesting gluten. It affects approximately 1% of the UK population, but only one in three people is thought to have a diagnosis. Untreated coeliac disease may lead to malnutrition, anaemia, osteoporosis and lymphoma.

OBJECTIVES

The objectives were to define at-risk groups and determine the cost-effectiveness of active case-finding strategies in primary care.

DESIGN

(1) Systematic review of the accuracy of potential diagnostic indicators for coeliac disease. (2) Routine data analysis to develop prediction models for identification of people who may benefit from testing for coeliac disease. (3) Systematic review of the accuracy of diagnostic tests for coeliac disease. (4) Systematic review of the accuracy of genetic tests for coeliac disease (literature search conducted in April 2021). (5) Online survey to identify diagnostic thresholds for testing, starting treatment and referral for biopsy. (6) Economic modelling to identify the cost-effectiveness of different active case-finding strategies, informed by the findings from previous objectives.

DATA SOURCES

For the first systematic review, the following databases were searched from 1997 to April 2021: MEDLINE® (National Library of Medicine, Bethesda, MD, USA), Embase® (Elsevier, Amsterdam, the Netherlands), Cochrane Library, Web of Science™ (Clarivate™, Philadelphia, PA, USA), the World Health Organization International Clinical Trials Registry Platform ( WHO ICTRP ) and the National Institutes of Health Clinical Trials database. For the second systematic review, the following databases were searched from January 1990 to August 2020: MEDLINE, Embase, Cochrane Library, Web of Science, Kleijnen Systematic Reviews ( KSR ) Evidence, WHO ICTRP and the National Institutes of Health Clinical Trials database. For prediction model development, Clinical Practice Research Datalink GOLD, Clinical Practice Research Datalink Aurum and a subcohort of the Avon Longitudinal Study of Parents and Children were used; for estimates for the economic models, Clinical Practice Research Datalink Aurum was used.

REVIEW METHODS

For review 1, cohort and case-control studies reporting on a diagnostic indicator in a population with and a population without coeliac disease were eligible. For review 2, diagnostic cohort studies including patients presenting with coeliac disease symptoms who were tested with serological tests for coeliac disease and underwent a duodenal biopsy as reference standard were eligible. In both reviews, risk of bias was assessed using the quality assessment of diagnostic accuracy studies 2 tool. Bivariate random-effects meta-analyses were fitted, in which binomial likelihoods for the numbers of true positives and true negatives were assumed.

RESULTS

People with dermatitis herpetiformis, a family history of coeliac disease, migraine, anaemia, type 1 diabetes, osteoporosis or chronic liver disease are 1.5-2 times more likely than the general population to have coeliac disease; individual gastrointestinal symptoms were not useful for identifying coeliac disease. For children, women and men, prediction models included 24, 24 and 21 indicators of coeliac disease, respectively. The models showed good discrimination between patients with and patients without coeliac disease, but performed less well when externally validated. Serological tests were found to have good diagnostic accuracy for coeliac disease. Immunoglobulin A tissue transglutaminase had the highest sensitivity and endomysial antibody the highest specificity. There was little improvement when tests were used in combination. Survey respondents (n = 472) wanted to be 66% certain of the diagnosis from a blood test before starting a gluten-free diet if symptomatic, and 90% certain if asymptomatic. Cost-effectiveness analyses found that, among adults, and using serological testing alone, immunoglobulin A tissue transglutaminase was most cost-effective at a 1% pre-test probability (equivalent to population screening). Strategies using immunoglobulin A endomysial antibody plus human leucocyte antigen or human leucocyte antigen plus immunoglobulin A tissue transglutaminase with any pre-test probability had similar cost-effectiveness results, which were also similar to the cost-effectiveness results of immunoglobulin A tissue transglutaminase at a 1% pre-test probability. The most practical alternative for implementation within the NHS is likely to be a combination of human leucocyte antigen and immunoglobulin A tissue transglutaminase testing among those with a pre-test probability above 1.5%. Among children, the most cost-effective strategy was a 10% pre-test probability with human leucocyte antigen plus immunoglobulin A tissue transglutaminase, but there was uncertainty around the most cost-effective pre-test probability. There was substantial uncertainty in economic model results, which means that there would be great value in conducting further research.

LIMITATIONS

The interpretation of meta-analyses was limited by the substantial heterogeneity between the included studies, and most included studies were judged to be at high risk of bias. The main limitations of the prediction models were that we were restricted to diagnostic indicators that were recorded by general practitioners and that, because coeliac disease is underdiagnosed, it is also under-reported in health-care data. The cost-effectiveness model is a simplification of coeliac disease and modelled an average cohort rather than individuals. Evidence was weak on the probability of routine coeliac disease diagnosis, the accuracy of serological and genetic tests and the utility of a gluten-free diet.

CONCLUSIONS

Population screening with immunoglobulin A tissue transglutaminase (1% pre-test probability) and of immunoglobulin A endomysial antibody followed by human leucocyte antigen testing or human leucocyte antigen testing followed by immunoglobulin A tissue transglutaminase with any pre-test probability appear to have similar cost-effectiveness results. As decisions to implement population screening cannot be made based on our economic analysis alone, and given the practical challenges of identifying patients with higher pre-test probabilities, we recommend that human leucocyte antigen combined with immunoglobulin A tissue transglutaminase testing should be considered for adults with at least a 1.5% pre-test probability of coeliac disease, equivalent to having at least one predictor. A more targeted strategy of 10% pre-test probability is recommended for children (e.g. children with anaemia).

FUTURE WORK

Future work should consider whether or not population-based screening for coeliac disease could meet the UK National Screening Committee criteria and whether or not it necessitates a long-term randomised controlled trial of screening strategies. Large prospective cohort studies in which all participants receive accurate tests for coeliac disease are needed.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42019115506 and CRD42020170766.

FUNDING

This project was funded by the National Institute for Health and Care Research ( NIHR ) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 44. See the NIHR Journals Library website for further project information.

Early economic modeling of magnetic resonance image-guided high intensity focused ultrasound compared to radiotherapy for pain palliation of bone metastases

Introduction

Magnetic Resonance Image-guided High Intensity Focused Ultrasound (MR-HIFU) is a non-invasive treatment option for palliative patients with painful bone metastases. Early evidence suggests that MR-HIFU is associated with similar overall treatment response, but more rapid pain palliation compared to external beam radiotherapy (EBRT). This modelling study aimed to assess the cost-effectiveness of MR-HIFU as an alternative treatment option for painful bone metastases from the perspective of the German Statutory Health Insurance (SHI).

Materials and methods

A microsimulation model with lifelong time horizon and one-month cycle length was developed. To calculate the incremental cost-effectiveness ratio (ICER), strategy A (MR-HIFU as first-line treatment or as retreatment option in case of persistent pain or only partial pain relief after EBRT) was compared to strategy B (EBRT alone) for patients with bone metastases due to breast, prostate, or lung cancer. Input parameters used for the model were extracted from the literature. Results were expressed as EUR per quality-adjusted life years (QALYs) and EUR per pain response (i.e., months spent with complete or partial pain response). Deterministic and probabilistic sensitivity analyses (PSA) were performed to test the robustness of results, and a value of information analysis was conducted.

Results

Compared to strategy B, strategy A resulted in additional costs (EUR 399) and benefits (0.02 QALYs and 0.95 months with pain response). In the base case, the resulting ICERs (strategy A vs. strategy B) are EUR 19,845/QALY and EUR 421 per pain response. Offering all patients MR-HIFU as first-line treatment would increase the ICER by 50% (31,048 EUR/QALY). PSA showed that at a (hypothetical) willingness to pay of EUR 20,000/QALY, the probability of MR-HIFU being cost-effective was 52%. The expected value of perfect information (EVPI) for the benefit population in Germany is approximately EUR 190 Mio.

Conclusion

Although there is considerable uncertainty, the results demonstrate that introducing MR-HIFU as a treatment alternative for painful bone metastases might be cost-effective for the German SHI. The high EVPI indicate that further studies to reduce uncertainty would be worthwhile.

Coverage with evidence development for medical devices in Europe: Can practice meet theory?

Health economists have written extensively on the design and implementation of coverage with evidence development (CED) schemes and have proposed theoretical frameworks based on cost-effectiveness modeling and value of information analysis. CED may aid decision-makers when there is uncertainty about the (cost-)effectiveness of a new health technology at the time of reimbursement. Medical devices are potential candidates for CED schemes, as regulatory regimes do not usually require the same level of efficacy and safety data normally needed for pharmaceuticals. The purpose of this research is to assess whether the actual practice of CED for medical devices in Europe meets the theoretical principles proposed by health economists and whether theory and practice can be more closely aligned. Based on decision-makers' perceptions of the challenges associated with CED schemes, plus examples from the schemes themselves, we discuss a series of proposals for assessing the desirability of schemes, their design, implementation, and evaluation. These proposals, while reflecting the practical challenges with developing CED programs, embody many of the principles suggested by economists and should support decision-makers in dealing with uncertainty about the real-world performance of devices.

Exploring the misalignment on the value of further research between payers and manufacturers. A case study on a novel total artificial heart

Payers and manufacturers can disagree on the appropriate level of evidence that is required for new medical devices, resulting in high societal costs due to decisions taken with sub-optimal information. A cost-effectiveness model of a hypothetical total artificial heart was built using data from the literature and the (simulated) results of a pivotal study. The expected value of perfect information (EVPI) was calculated from both the payer and manufacturer perspectives, using net monetary benefit and the company's return on investment respectively. A function was also defined, linking effectiveness to market shares. Additional constraints such as a minimum clinical difference or maximum budget impact were introduced into the company's decisions to simulate additional barriers to adoption. The difference in the EVPI between manufacturers and payers varied greatly depending on the underlying decision rules and constraints. The manufacturer's EVPI depends on the probability of being reimbursed, the uncertainty on the (cost-)effectiveness of the technology, as well as other parameters relating to initial investments, operating costs and market dynamics. The use of Value of information for both perspectives can outline potential misalignments and can be particularly useful to inform early dialogs between manufacturers and payers, or negotiations on conditional reimbursement schemes.

Practical guidance for planning resources required to support publicly-funded adaptive clinical trials

Adaptive designs are a class of methods for improving efficiency and patient benefit of clinical trials. Although their use has increased in recent years, research suggests they are not used in many situations where they have potential to bring benefit. One barrier to their more widespread use is a lack of understanding about how the choice to use an adaptive design, rather than a traditional design, affects resources (staff and non-staff) required to set-up, conduct and report a trial. The Costing Adaptive Trials project investigated this issue using quantitative and qualitative research amongst UK Clinical Trials Units. Here, we present guidance that is informed by our research, on considering the appropriate resourcing of adaptive trials. We outline a five-step process to estimate the resources required and provide an accompanying costing tool. The process involves understanding the tasks required to undertake a trial, and how the adaptive design affects them. We identify barriers in the publicly funded landscape and provide recommendations to trial funders that would address them. Although our guidance and recommendations are most relevant to UK non-commercial trials, many aspects are relevant more widely.

Cancer Health Economics Research: The Future Is Now

The goals of the "Future of Cancer Health Economics Research" virtual conference were to identify challenges, gaps, and unmet needs for conducting cancer health economics research; and develop suggestions and ideas to address these challenges and to support the development of this field. The conference involved multiple presentations and panels featuring several key themes, including data limitations and fragmentation; improving research methods; role and impacts of structural and policy factors; and the transdisciplinary nature of this field. The conference also highlighted emerging areas such as communicating results with nonresearchers; balancing data accessibility and data security; emphasizing the needs of trainees; and including health equity as a focus in cancer health economics research. From this conference, it is clear that cancer health economics research can have substantial impacts on how cancer care is delivered and how related health-care policies are developed and implemented. To support further growth and development, this field should continue to welcome individuals from multiple disciplines and enhance opportunities for training in economics and in analytic methods and perspectives from across the social and clinical sciences. Researchers should continue to engage with diverse stakeholders throughout the cancer community, building collaborations and focusing on the goal of improving health and well-being.

A critical assessment framework to identify, quantify and interpret the sources of uncertainty in cost-effectiveness analyses

BACKGROUND

Using a standardized approach to describe the sources of uncertainty in cost-effectiveness analyses might bring added value to the local critical assessment procedure of reimbursement submissions in Hungary. The aim of this research is to present a procedural framework to identify, quantify and interpret sources of uncertainty, using the reimbursement dossier of darolutamide as an illustrative example.

METHODS

In the procedural framework designed for the critical assessment of cost-effectiveness analyses, the quantifiability of an identified source of uncertainty is assessed through the input parameters of the originally submitted model, which is followed by the interpretation of its impact on estimates of costs and outcomes compared to the base case cost-effectiveness conclusion.

RESULTS

Based on our experiences with the recent reimbursement dossier of darolutamide, the significant and quantifiable sources of uncertainty were the time horizon of the economic analysis; the restriction of the efficacy analysis population; long-term relative effectiveness of darolutamide; price discount on subsequent therapies. We identified resource use patterns for comparator and subsequent therapies as a quantifiable, yet non-significant source of uncertainty. The EQ-5D value set used to estimate utility values was identified as a non-quantifiable and potentially not significant source of uncertainty.

CONCLUSIONS

The procedural framework, demonstrated with an example, was sufficiently flexible and coherent to document and structure the sources of uncertainty in cost-effectiveness analyses. The full-scale use of this framework is desirable during the decision-making process for reimbursement in Hungary. The further formalization of identifying sources of uncertainty is a possible subject of methodological development.

Methods for Communicating the Impact of Parameter Uncertainty in a Multiple-Strategies Cost-Effectiveness Comparison

Purpose

Analyzing and communicating uncertainty is essential in medical decision making. To judge whether risks are acceptable, policy makers require information on the expected outcomes but also on the uncertainty and potential losses related to the chosen strategy. We aimed to compare methods used to represent the impact of uncertainty in decision problems involving many strategies, enhance existing methods, and provide an open-source and easy-to-use tool.

Methods

We conducted a systematic literature search to identify methods used to represent the impact of uncertainty in cost-effectiveness analyses comparing multiple strategies. We applied the identified methods to probabilistic sensitivity analysis outputs of 3 published decision-analytic models comparing multiple strategies. Subsequently, we compared the following characteristics: type of information conveyed, use of a fixed or flexible willingness-to-pay threshold, output interpretability, and the graphical discriminatory ability. We further proposed adjustments and integration of methods to overcome identified limitations of existing methods.

Results

The literature search resulted in the selection of 9 methods. The 3 methods with the most favorable characteristics to compare many strategies were 1) the cost-effectiveness acceptability curve (CEAC) and cost-effectiveness acceptability frontier (CEAF), 2) the expected loss curve (ELC), and 3) the incremental benefit curve (IBC). The information required to assess confidence in a decision often includes the average loss and the probability of cost-effectiveness associated with each strategy. Therefore, we proposed the integration of information presented in an ELC and CEAC into a single heat map.

Conclusions

This article presents an overview of methods presenting uncertainty in multiple-strategy cost-effectiveness analyses, with their strengths and shortcomings. We proposed a heat map as an alternative method that integrates all relevant information required for health policy and medical decision making.

Highlights

Risk-benefit analysis of emergency vaccine use

Emergency vaccine use requires weighing a large number of uncertain risks and possible benefits. In the COVID-19 pandemic, decisions about what evidence is necessary to authorize emergency use have proven controversial, and vary between countries. We construct a simple mathematical model of the risks and benefits of emergency vaccination to an individual, and apply this to the hypothetical scenario of individual decision-making between emergency use of a COVID-19 vaccine without safety and efficacy data, versus waiting for efficacy and safety to be established. Even with conservative modelling assumptions and uncertainty distributions for vaccine efficacy (mean expectation = 17%) and serious adverse event risk (mean expectation = 0.3%), high risk individuals (e.g. those who are elderly and have a household contact with COVID-19) are better off using the ’emergency vaccine’ rather than waiting for more information (absolute risk reduction for mortality up to 2%). Very early emergency authorization of vaccines despite very limited data may be the better public health strategy when confronted with a dangerous emerging infectious disease.

A cost-effectiveness and value of information analysis to inform future research of tranexamic acid for older adults experiencing mild traumatic brain injury

BACKGROUND

Tranexamic acid reduces head injury deaths in patients with CT scan evidence of intracranial bleeding after mild traumatic brain injury (TBI). However, the cost-effectiveness of tranexamic acid for people with mild TBI in the pre-hospital setting, prior to CT scanning, is uncertain. A large randomised controlled trial (CRASH-4) is planned to address this issue, but the economic justification for it has not been established. The aim of the analysis was to estimate the likelihood of tranexamic acid being cost-effective given current evidence, the treatment effects required for cost-effectiveness, and the expected value of performing further research.

METHODS

An early economic decision model compared usual care for mild TBI with and without tranexamic acid, for adults aged 70 and above. The evaluation was performed from a UK healthcare perspective over a lifetime time horizon, with costs reported in 2020 pounds (GBP) and outcomes reported as quality-adjusted life years (QALYs). All analyses used a £20,000 per QALY cost-effectiveness threshold.

RESULTS

In the base case analysis, tranexamic acid was associated with an incremental cost-effectiveness ratio of £4885 per QALY gained, but the likelihood of it being cost-effective was highly dependent on the all-cause mortality treatment effect. The value of perfect information was £22.4 million, and the value of perfect information for parameters that could be collected in a trial was £21.9 million. The all-cause mortality risk ratio for tranexamic acid and the functional outcomes following TBI had the most impact on cost-effectiveness.

CONCLUSIONS

There is a high degree of uncertainty in the cost-effectiveness of tranexamic acid for older adults experiencing mild TBI, meaning there is a high value of performing future research in the UK. The value in a global context is likely to be far higher.