Reply to Standaert, B. Comment on "Postma et al. Predicted Public Health and Economic Impact of Respiratory Syncytial Virus Vaccination with Variable Duration of Protection for Adults ≥60 Years in Belgium".

We have read the commentary from Baudouin Standaert [...].

Extrapolation of Survival Data Using a Bayesian Approach: A Case Study Leveraging External Data from Cilta-Cel Therapy in Multiple Myeloma.

INTRODUCTION: Extrapolating long-term overall survival (OS) from shorter-term clinical trial data is key to health technology assessment in oncology. However, extrapolation using conventional methods is often subject to uncertainty. Using ciltacabtagene autoleucel (cilta-cel), a chimeric antigen receptor T-cell therapy for multiple myeloma, we used a flexible Bayesian approach to demonstrate use of external longer-term data to reduce the uncertainty in long-term extrapolation. METHODS: The pivotal CARTITUDE-1 trial (NCT03548207) provided the primary efficacy data for cilta-cel, including a 12-month median follow-up snapshot of OS. Longer-term (48-month median follow-up) survival data from the phase I LEGEND-2 study (NCT03090659) were also available. Twelve-month CARTITUDE-1 OS data were extrapolated in two ways: (1) conventional survival models with standard parametric distributions (uninformed), and (2) Bayesian survival models whose shape prior was informed from 48-month LEGEND-2 data. For validation, extrapolations from 12-month CARTITUDE-1 data were compared with observed 28-month CARTITUDE-1 data. RESULTS: Extrapolations of the 12-month CARTITUDE-1 data using conventional uninformed parametric models were highly variable. Using informative priors from the 48-month LEGEND-2 dataset, the ranges of projected OS at different timepoints were consistently narrower. Area differences between the extrapolation curves and the 28-month CARTITUDE-1 data were generally lower in informed Bayesian models, except for the uninformed log-normal model, which had the lowest difference. CONCLUSIONS: Informed Bayesian survival models reduced variation of long-term projections and provided similar projections as the uninformed log-normal model. Bayesian models generated a narrower and more plausible range of OS projections from 12-month data that aligned with observed 28-month data. TRIAL REGISTRATION: CARTITUDE-1 ClinicalTrials.gov identifier, NCT03548207. LEGEND-2 ClinicalTrials.gov identifier, NCT03090659, registered retrospectively on 27 March 2017, and ChiCTR-ONH-17012285.

Predicted Public Health and Economic Impact of Respiratory Syncytial Virus Vaccination with Variable Duration of Protection for Adults ≥60 Years in Belgium.

Respiratory syncytial virus (RSV) is a leading cause of acute respiratory infection (ARI) in older adults. This study used a static, cohort-based decision-tree model to estimate the public health and economic impact of vaccination against RSV in Belgians aged ≥60 years compared with no vaccination for different vaccine duration of protection profiles from a healthcare payer perspective. Three vaccine protection durations were compared (1, 3, and 5 years), and several sensitivity and scenario analyses were performed. Results showed that an RSV vaccine with a 3-year duration of protection would prevent 154,728 symptomatic RSV-ARI cases, 3688 hospitalizations, and 502 deaths over three years compared to no vaccination in older adults and would save EUR 35,982,857 in direct medical costs in Belgium. The number needed to vaccinate to prevent one RSV-ARI case was 11 for the 3-year duration profile, while it was 28 and 8 for the 1- and 5-year vaccine duration profiles, respectively. The model was generally robust in sensitivity analyses varying key input values. This study suggested that vaccination could substantially decrease the public health and economic burden of RSV in adults ≥60 years in Belgium, with benefits increasing with a longer duration of vaccine protection.

Economic Burden of COVID-19: A Systematic Review.

Objective: To review and qualitatively synthesize the evidence related to the economic burden of COVID-19, including healthcare resource utilization and costs. Methods: A systematic review of studies that assessed the economic burden [eg, direct costs, productivity, macroeconomic impact due to non-pharmaceutical interventions (NPIs) and equity] of COVID-19 was conducted by searches in EMBASE, MEDLINE, MEDLINE-IN-PROCESS, and The Cochrane Library, as well as manual searches of unpublished research for the period between January 2020 to February 2021. Single reviewer data extraction was confirmed independently by a second reviewer. Results: The screening process resulted in a total of 27 studies: 25 individual publications, and 2 systematic literature reviews, of narrower scopes, that fulfilled the inclusion criteria. The patients diagnosed with more severe COVID-19 were associated with higher costs. The main drivers for higher costs were consistent across countries and included ICU admission, in-hospital resource use such as mechanical ventilation, which lead to increase costs of $2082.65 ± 345.04 to $2990.76 ± 545.98. The most frequently reported indirect costs were due to productivity losses. On average, older COVID-19 patients incurred higher costs when compared to younger age groups. An estimation of a 20% COVID-19 infection rate based on a Monte Carlo simulation in the United States led to a total direct medical cost of $163.4 billion over the course of the pandemic. Conclusion: The COVID-19 pandemic has generated a considerable economic burden on patients and the general population. Preventative measures such as NPIs only have partial success in lowering the economic costs of the pandemic. Implementing additional preventative measures such as large-scale vaccination is vital in reducing direct and indirect medical costs, decreased productivity, and GDP losses.

10 Years of End-of-Life Criteria in the United Kingdom.

OBJECTIVES: In January 2009, the National Institute for Health and Care Excellence introduced supplementary guidance for end-of-life (EoL) treatments, which allowed treatments with an incremental cost-effectiveness ratio over the regular threshold (£20 000-£30 000) to be recommended, if they satisfied the EoL criteria. The aims of this study were (1) to systematically review 10 years of EoL supplementary guidance implementation and explore how it could be improved, and (2) to create a framework for incorporating the uncertainty relating to EoL criteria satisfaction into model-based cost-effectiveness analyses for decision making. METHODS: All appraisals between January 2009 and 2019 were screened for EoL discussions. Data were extracted on the EoL criteria and cost-effectiveness assessment details. Additionally, a quantitative method was developed to include the EoL criteria satisfaction uncertainty into model-based cost-effectiveness analyses. A stylized example was created to provide a case study for the inclusion of EoL criteria satisfaction uncertainty. RESULTS: An EoL discussion was identified in 35% of appraisals, 57% of which led to a positive EoL decision. Only 5.7% of technologies with positive EoL decisions were not recommended, versus 43.8% of technologies with negative EoL decisions. EoL criteria assessment was often reported insufficiently and evaluated inconsistently and nontransparently. A total of 54.9% of EoL decisions were made while at least 1 criterion was surrounded by considerable uncertainty. By applying the proposed quantitative method, this EoL criteria satisfaction uncertainty was accounted for in decision making. The stylized example demonstrated that the impact of EoL criteria satisfaction uncertainty can be substantial enough to reverse the reimbursement decision. CONCLUSIONS: To improve consistency/transparency and correct reimbursement decisions' likelihood, new guidelines on the implementation of the EoL criteria are needed.

A MEA is a MEA is a MEA? Sequential decision making and the impact of different managed entry agreements at the manufacturer and payer level, using a case study for an oncology drug in England.

BACKGROUND: In a typical single-payer setting that uses an explicit cost-effectiveness (CE) threshold in its decision-making, the payer aims to maximize the net-monetary-benefit (NMB) given the CE threshold, whilst the manufacturer aims to maximize the expected discounted-cash-flow (DCF) resulting from the sales of that technology. Managed entry agreements (MEAs) are tools that are used to improve access to expensive technologies that would otherwise not be deemed to be cost-effective to payers. While simple discount on the list price is the most commonly applied MEA type, there are different forms, each having a different impact on the cost-effectiveness of the technology, on the lifetime DCF-per-patient and on the decision uncertainty. We aim to analyze the sequential decision-making (SDM) of different MEAs (i.e. simple discount, free treatment initiation, lifetime treatment acquisition cost-capping [LTTACC], performance-based money-back guarantee [MBG]) at the manufacturer and at the payer level, respectively. METHODS: We first model the SDM of the manufacturer and the payer as a sequential game and explain the challenges to find an equilibrium analytically. Then we propose a heuristic computational method to follow for each of the MEA types, based on practice. To demonstrate this SDM on a case study, a UK-based cost-utility analysis using a three-state, partitioned-survival-model was constructed to determine the cost-effectiveness of regorafenib versus best-supportive-care for the second-line treatment of hepatocellular carcinoma. The optimal agreement terms that would maximise the lifetime DCF-per-patient for each MEA, whilst remaining below the CE-threshold (£50,000/QALY gained) were obtained in the deterministic base-case. Robustness for each optimized MEA was then assessed using probabilistic sensitivity and scenario analyses, the value of information (VoI), and HTA-risk analyses. RESULTS: As expected, the introduction of all MEAs improved the probabilistic ICER and NMB values to (almost) acceptable levels, compared to the "no-MEA" case (ICER ~ £78,000/QALY-gained). The expected DCFs across the explored MEAs were all similar, whilst the payer strategy & uncertainty burden (PSUB) for regorafenib decreased in all MEAs explored. VoI analyses revealed that regorafenib mean-dose-intensity and time-on-treatment (ToT) parameters attributed most to the decision uncertainty. LTTACC provided the smallest PSUB and the most robust NMB estimates under parametric uncertainty. For scenarios assuming increased regorafenib ToT or mean-dose-intensity, LTACC again provided acceptable cost-effectiveness outcomes, whereas for scenarios assuming decreased regorafenib progression-free/overall survival effectiveness, only MBG resulted in plausible ICER values. In scenarios, where the source of uncertainty was not targeted by MEA parameters (e.g. the scenario assuming higher progressed disease resource utilization), all investigated MEA types resulted in unacceptable cost-effectiveness outcomes. CONCLUSION: Each MEA type has a different implication. The impact of different MEAs on the NMB is more noteworthy than on the DCF, in relative terms, hence payers will benefit from the early participation of the MEA design rather than leaving this up to the prerogative of the manufacturer. While simple discount might be practical for implementation purposes, other MEAs can provide additional benefits to the payer in terms of increased NMB, reduced decision risk and reduced uncertainty. MEA performance should be investigated not only under parametric uncertainty, but also under-identified structural uncertainty, and the barriers of implementation should be considered thoroughly before choosing the most appropriate MEA type.

The Methodological Quality and Challenges in Conducting Economic Evaluations of Newborn Screening: A Scoping Review.

INTRODUCTION: Cost-effectiveness (CEA) and cost-utility analyses (CUA) have become popular types of economic evaluations (EE) used for evidence-based decision-making in healthcare resource allocation. Newborn screening programs (NBS) can have significant clinical benefits for society, and cost-effectiveness analysis may help to select the optimal strategy among different screening programs, including the no-screening option, on different conditions. These economic analyses of NBS, however, are hindered by several methodological challenges. This study explored the methodological quality in recent NBS economic evaluations and analyzed the main challenges and strategies adopted by researchers to deal with them. METHODS: A scoping review was conducted according to PRISMA methodology to identify CEAs and CUAs of NBS. The methodological quality of the retrieved studies was assessed quantitatively using a specific guideline for the quality assessment of NBS economic evaluations, by calculating a general score for each EE. Challenges in the studies were then explored using thematic analysis as a qualitative synthesis approach. RESULTS: Thirty-five studies met the inclusion criteria. The quantitative analysis showed that the methodological quality of NBS economic evaluations was heterogeneous. Lack of clear description of items related to results, discussion, and discounting were the most frequent flaws. Methodological challenges in performing EEs of neonatal screenings include the adoption of a long time horizon, the use of quality-adjusted life years as health outcome measure, and the assessment of costs beyond the screening interventions. CONCLUSIONS: The results of this review can support future economic evaluation research, aiding researchers to develop a methodological guidance to perform EEs aimed at producing solid results to inform decisions for resource allocation in neonatal screening.

Avatrombopag and lusutrombopag for thrombocytopenia in people with chronic liver disease needing an elective procedure: a systematic review and cost-effectiveness analysis.

BACKGROUND: There have been no licensed treatment options in the UK for treating thrombocytopenia in people with chronic liver disease requiring surgery. Established management largely involves platelet transfusion prior to the procedure or as rescue therapy for bleeding due to the procedure. OBJECTIVES: To assess the clinical effectiveness and cost-effectiveness of two thrombopoietin receptor agonists, avatrombopag (Doptelet®; Dova Pharmaceuticals, Durham, NC, USA) and lusutrombopag (Mulpleta®; Shionogi Inc., London, UK), in addition to established clinical management compared with established clinical management (no thrombopoietin receptor agonist) in the licensed populations. DESIGN: Systematic review and cost-effectiveness analysis. SETTING: Secondary care. PARTICIPANTS: Severe thrombocytopenia (platelet count of < 50,000/µl) in people with chronic liver disease requiring surgery. INTERVENTIONS: Lusutrombopag 3 mg and avatrombopag (60 mg if the baseline platelet count is < 40,000/µl and 40 mg if it is 40,000-< 50,000/µl). MAIN OUTCOME MEASURES: Risk of platelet transfusion and rescue therapy or risk of rescue therapy only. REVIEW METHODS: Systematic review including meta-analysis. English-language and non-English-language articles were obtained from several databases including MEDLINE, EMBASE and Cochrane Central Register of Controlled Trials, all searched from inception to 29 May 2019. ECONOMIC EVALUATION: Model-based cost-effectiveness analysis. RESULTS: From a comprehensive search retrieving 11,305 records, six studies were included. Analysis showed that avatrombopag and lusutrombopag were superior to no thrombopoietin receptor agonist in avoiding both platelet transfusion and rescue therapy or rescue therapy only, and mostly with a statistically significant difference (i.e. 95% confidence intervals not overlapping the point of no difference). However, only avatrombopag seemed to be superior to no thrombopoietin receptor agonist in reducing the risk of rescue therapy, although far fewer patients in the lusutrombopag trials than in the avatrombopag trials received rescue therapy. When assessing the cost-effectiveness of lusutrombopag and avatrombopag, it was found that, despite the success of these in avoiding platelet transfusions prior to surgery, the additional long-term gain in quality-adjusted life-years was very small. No thrombopoietin receptor agonist was clearly cheaper than both lusutrombopag and avatrombopag, as the cost savings from avoiding platelet transfusions were more than offset by the drug cost. The probabilistic sensitivity analysis showed that, for all thresholds below £100,000, no thrombopoietin receptor agonist had 100% probability of being cost-effective. LIMITATIONS: Some of the rescue therapy data for lusutrombopag were not available. There were inconsistencies in the avatrombopag data. From the cost-effectiveness point of view, there were several additional important gaps in the evidence required, including the lack of a price for avatrombopag. CONCLUSIONS: Avatrombopag and lusutrombopag were superior to no thrombopoietin receptor agonist in avoiding both platelet transfusion and rescue therapy, but they were not cost-effective given the lack of benefit and increase in cost. FUTURE WORK: A head-to-head trial is warranted. STUDY REGISTRATION: This study is registered as PROSPERO CRD42019125311. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 51. See the NIHR Journals Library website for further project information.

Thrombocytopenia, which is a reduction in platelet numbers in the blood, is a common complication of chronic liver disease. It increases the risk of bleeding during procedures including liver biopsy and transplantation. It can delay or prevent procedures, leading to illness and death. Established treatment largely involves platelet transfusion before the procedure or as rescue therapy for bleeding. This report aims to systematically review the clinical effectiveness and estimate the cost-effectiveness of the first two recently licensed treatments, thrombopoietin receptor agonists avatrombopag (Doptelet^®; Dova Pharmaceuticals, Durham, NC, USA) (60 mg if platelet count is < 40,000/µl and 40 mg if platelet count is 40,000­< 50,000/µl) and lusutrombopag (Mulpleta^®; Shionogi Inc., London, UK) (3 mg if platelet count is < 50,000/µl), compared with established treatment. From a comprehensive search, six studies were included. Clinical effectiveness analysis showed that avatrombopag and lusutrombopag were superior to no thrombopoietin receptor agonist in avoiding both platelet transfusion and rescue therapy. Only avatrombopag seemed superior to no thrombopoietin receptor agonist in reducing rescue therapy alone. Cost-effectiveness analysis found that lusutrombopag and avatrombopag were more expensive than no thrombopoietin receptor agonist over a lifetime, as the savings from avoiding platelet transfusions were exceeded by the drug cost, and without long-term health benefits. The probabilistic sensitivity analysis, which examined the effect of uncertainty, showed that no thrombopoietin receptor agonist had 100% probability of being cost-effective. Uncertainty about the price of avatrombopag and the content and costs of platelet transfusions and the potential under-reporting of use to estimate platelet transfusion-specific mortality had the greatest impact on results. If the price of avatrombopag was (confidential information has been removed) below the price of lusutrombopag, avatrombopag would become cost saving in the 40,000­< 50,000/µl subgroup. However, although in some scenarios avatrombopag costs could decrease in the 40,000­< 50,000/µl subgroup to around 10% more than the cost of no thrombopoietin receptor agonist, there would be negligible health benefits and the incremental cost-effectiveness ratios would remain very high, meaning that lusutrombopag and avatrombopag would still not be considered cost-effective.

TECH-VER: A Verification Checklist to Reduce Errors in Models and Improve Their Credibility.

BACKGROUND: In health economic literature, checklists or best practice recommendations on model validation/credibility always declare verification of the programmed model as a fundamental step, such as 'is the model implemented correctly and does the implementation accurately represent the conceptual model?' However, to date, little operational guidance for the model verification process has been given. In this study, we aimed to create an operational checklist for model users or reviewers to verify the technical implementation of health economic decision analytical models and document their verification efforts. METHODS: Literature on model validation, verification, programming errors and credibility was reviewed systematically from scientific databases. An initial beta version of the checklist was developed based on the checklists/tests identified from the literature and from authors' previous modeling/appraisal experience. Next, the first draft checklist was presented to a number of health economists on several occasions and was tested on different models (built in different software, developed by different stakeholders, including drug manufacturers, consultancies or academia), each time leading to an update of the checklist and culminating in the final version of the TECHnical VERification (TECH-VER) checklist, introduced in this paper. RESULTS: The TECH-VER necessitates a model reviewer (preferably independent), an executable and transparent model, its input sources, and detailed documentation (e.g. technical report/scientific paper) in which the conceptual model, its implementation, programmed model inputs, and results are reported. The TECH-VER checklist consists of five domains: (1) input calculations; (2) event-state (patient flow) calculations; (3) result calculations; (4) uncertainty analysis calculations; and (5) other overall checks (e.g. validity or interface). The first four domains reflect the verification of the components of a typical health economic model. For these domains, as a prerequisite of verification tests, the reviewer should identify the relevant calculations in the electronic model and assess the provided justifications for the methods used in the identified calculations. For this purpose, we recommend completeness/consistency checks. Afterwards, the verification tests can be conducted for the calculations in each of these stages by checking the correctness of the implementation of these calculations. For this purpose, the following type of tests are recommended in consecutive order: (i) black-box tests, i.e. checking if model calculations are in line with a priori expectations; (ii) white-box testing, i.e. going through the program code details line by line, or cell by cell (recommended for some crucial calculations and if there are some unexpected results from the black-box tests); and (iii) model replication/parallel programming (recommended only in certain situations, and if the issues related to the identified unexpected results from black-box tests could not be resolved through white-box testing). To reduce the time burden of model verification, we suggest a hierarchical order in tests i-iii, where going to the next step is necessary when the previous step fails. CONCLUSIONS: The TECH-VER checklist is a comprehensive checklist for the technical verification of decision analytical models, aiming to help identify model implementation errors and their root causes while improving the transparency and efficiency of the verification efforts. In addition to external reviews, we consider that the TECH-VER can be used as an internal training and quality control tool for new health economists, while developing their initial models. It is the authors' aim that the TECH-VER checklist transforms itself to an open-source living document, with possible future versions, or 'bolt-on' extensions for specific applications with additional 'fit-for-purpose' tests, as well as 'tips and tricks' and some demonstrative error examples. For this reason, the TECH-VER checklist and the list of black-box tests created in this paper and a few model verification examples is uploaded to an open access, online platform (github and the website of the institute), where other users will also be able to upload their original verification efforts and tests.

Value of information analysis in telehealth for chronic heart failure management.

OBJECTIVES: Value of information (VOI) analysis provides information on opportunity cost of a decision in healthcare by estimating the cost of reducing parametric uncertainty and quantifying the value of generating additional evidence. This study is an application of the VOI methodology to the problem of choosing between home telemonitoring and nurse telephone support over usual care in chronic heart failure management in the Netherlands. METHODS: The expected value of perfect information (EVPI) and the expected value of partially perfect information (EVPPI) analyses were based on an informal threshold of €20K per quality-adjusted life-year. These VOI-analyses were applied to a probabilistic Markov model comparing the 20-year costs and effects in three interventions. The EVPPI explored the value of decision uncertainty caused by the following group of parameters: treatment-specific transition probabilities between New York Heart Association (NYHA) defined disease states, utilities associated with the disease states, number of hospitalizations and ER visits, health state specific costs, and the distribution of patients per NYHA group. We performed the analysis for two population sizes in the Netherlands-patients in all NYHA classes of severity, and patients in NYHA IV class only. RESULTS: The population EVPI for an effective population of 2,841,567 CHF patients in All NYHA classes of severity over the next 20 years is more than €4.5B, implying that further research is highly cost-effective. In the NYHA IV only analysis, for the effective population of 208,003 patients over next 20 years, the population EVPI at the same informal threshold is approx. €590M. The EVPPI analysis showed that the only relevant group of parameters that contribute to the overall decision uncertainty are transition probabilities, in both All NYHA and NYHA IV analyses. CONCLUSIONS: Results of our VOI exercise show that the cost of uncertainty regarding the decision on reimbursement of telehealth interventions for chronic heart failure patients is high in the Netherlands, and that future research is needed, mainly on the transition probabilities.