Consolidated Health Economic Evaluation Reporting Standards for Interventions that use Artificial Intelligence (CHEERS-AI).

OBJECTIVES: Economic evaluations (EEs) are commonly used by decision makers to understand the value of health interventions. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS 2022) provide reporting guidelines for EEs. Healthcare systems will increasingly see new interventions that use artificial intelligence (AI) to perform their function. We developed CHEERS-AI to ensure EEs of AI-based health interventions are reported in a transparent and reproducible manner. METHODS: Potential CHEERS-AI reporting items were informed by 2 published systematic literature reviews of EEs and a contemporary update. A Delphi study was conducted using 3 survey rounds to elicit multidisciplinary expert views on 26 potential items, through a 9-point Likert rating scale and qualitative comments. An online consensus meeting was held to finalise outstanding reporting items. A digital health patient group reviewed the final checklist from a patient perspective. RESULTS: A total of 58 participants responded to survey round 1, 42 and 31 of whom responded to rounds 2 and 3, respectively. Nine participants joined the consensus meeting. Ultimately, 38 reporting items were included in CHEERS-AI. They comprised the 28 original CHEERS 2022 items, plus 10 new AI-specific reporting items. Additionally, 8 of the original CHEERS 2022 items were elaborated on to ensure AI-specific nuance is reported. CONCLUSIONS: CHEERS-AI should be used when reporting an EE of an intervention that uses AI to perform its function. CHEERS-AI will help decision makers and reviewers to understand important AI-specific details of an intervention, and any implications for the EE methods used and cost-effectiveness conclusions.

A systematic review of economic evaluations of pharmacological treatments for active tuberculosis.

Objectives: The continuing spread of tuberculosis (TB) worldwide, especially drug-resistant TB, poses a major challenge to healthcare systems globally. Addressing this requires appraising the cost effectiveness of existing pharmacological interventions against TB to identify key drivers of cost effectiveness and value and guide pharmaceutical innovation and novel drug regimen development. Methods: Studies were identified from a search of six database: MEDLINE MEDLINE-In Process, MEDLINE Epub Ahead of Print, EMBASE, Cochrane Database of Systematic Reviews, and Econlit in July 2022. Two reviewers independently assessed all identified studies and reports using pre-defined inclusion/exclusion criteria. Study methodological quality was assessed, data were extracted in standard tables, and results were narratively synthesized. Results: Overall, 991 studies and 53 HTA reports were identified with 20 studies and 3 HTA reports meeting the inclusion criteria. Quality assessment of the 20 studies identified 4 with minor limitations, while the remainder were assessed as having potentially or very serious limitations. Sixteen studies conducted cost-utility analyses, 6 conducted cost-effectiveness analyses, and 2 conducted cost-comparison analyses with some studies performing multiple analyses. The majority (n = 16) were model-based. Eleven studies analyzed the cost-effectiveness of bedaquiline, 6 compared shorter to longer/standard duration regimens, 2 assessed ethambutol, and 1 assessed delamanid. Key drivers of cost effectiveness were drug costs, the number of TB cases, the portion of cases with sputum culture conversion, treatment delivery costs, and treatment efficacy. Common value elements considered included adverse events, drug resistance, and improving treatment adherence. Conclusion: Our results suggest that out of the pharmacological treatments assessed, bedaquiline is likely a cost-effective addition to existing treatment regimens/background treatment regimens, while ethambutol is not likely to be. Newer shorter regimens, even if more costly, seem to be more cost-effective compared to longer regimens. These results illustrate the limited number of novel cost-effective pharmacological interventions and highlight a need to develop new drugs/regimens against TB to overcome resistance, taking into account the key drivers of cost effectiveness and other value attributes identified from this review.

Markov modeling for cost-effectiveness using federated health data network.

OBJECTIVE: To introduce 2 R-packages that facilitate conducting health economics research on OMOP-based data networks, aiming to standardize and improve the reproducibility, transparency, and transferability of health economic models. MATERIALS AND METHODS: We developed the software tools and demonstrated their utility by replicating a UK-based heart failure data analysis across 5 different international databases from Estonia, Spain, Serbia, and the United States. RESULTS: We examined treatment trajectories of 47 163 patients. The overall incremental cost-effectiveness ratio (ICER) for telemonitoring relative to standard of care was 57 472 €/QALY. Country-specific ICERs were 60 312 €/QALY in Estonia, 58 096 €/QALY in Spain, 40 372 €/QALY in Serbia, and 90 893 €/QALY in the US, which surpassed the established willingness-to-pay thresholds. DISCUSSION: Currently, the cost-effectiveness analysis lacks standard tools, is performed in ad-hoc manner, and relies heavily on published information that might not be specific for local circumstances. Published results often exhibit a narrow focus, central to a single site, and provide only partial decision criteria, limiting their generalizability and comprehensive utility. CONCLUSION: We created 2 R-packages to pioneer cost-effectiveness analysis in OMOP CDM data networks. The first manages state definitions and database interaction, while the second focuses on Markov model learning and profile synthesis. We demonstrated their utility in a multisite heart failure study, comparing telemonitoring and standard care, finding telemonitoring not cost-effective.

Methods for Including Adverse Events in Economic Evaluations: Suggestions for Improvement.

OBJECTIVE: Inclusion of relevant effectiveness and safety outcomes in economic evaluation of health technologies is required to aid efficient healthcare decisions. Our objective was to identify the key issues related to the inclusion of adverse events (AEs) in economic evaluation and explore perspectives for good practice recommendations to handle these issues. METHODS: We focused on the frequently encountered methodological issues related to the integration of AEs in economic evaluations of health technologies. We distinguished the following elements: the incorporation of AEs in decision models, the terminology of AEs, the estimation of AEs consequences in terms of quality of life (QoL) and costs, and the exploration of the uncertainty related to the impact of AEs on the economic results. RESULTS: We illustrated and discussed each of the identified issues by giving health technology assessment examples. We focused on the extent to which the integration of AEs in decision models can be improved by dealing with the lack of relevant real-world safety data, estimating the consequences of AEs (eg, for costs and QoL loss), exploring the impacts of AEs that are not adequately captured in current measurement of health-related QoL, and identifying the need for development of a good terminology of relevant types of AEs to be incorporated in economic evaluation. CONCLUSION: Based on a reflection the key methodological issues related to the incorporation of adverse drug events in economic evaluations, we suggested several recommendations to serve a starting point for health technology assessment agencies and researchers to develop good research practices in this field.

Acceptability of Using Real-World Data to Estimate Relative Treatment Effects in Health Technology Assessments: Barriers and Future Steps.

OBJECTIVES: Evidence about the comparative effects of new treatments is typically collected in randomized controlled trials (RCTs). In some instances, RCTs are not possible, or their value is limited by an inability to capture treatment effects over the longer term or in all relevant population subgroups. In these cases, nonrandomized studies (NRS) using real-world data (RWD) are increasingly used to complement trial evidence on treatment effects for health technology assessment (HTA). However, there have been concerns over a lack of acceptability of this evidence by HTA agencies. This article aims to identify the barriers to the acceptance of NRS and steps that may facilitate increases in the acceptability of NRS in the future. METHODS: Opinions of the authorship team based on their experience in real-world evidence research in academic, HTA, and industry settings, supported by a critical assessment of existing studies. RESULTS: Barriers were identified that are applicable to key stakeholder groups, including HTA agencies (eg, the lack of comprehensive methodological guidelines for using RWD), evidence generators (eg, avoidable deviations from best practices), and external stakeholders (eg, data controllers providing timely access to high-quality RWD). Future steps that may facilitate future acceptability of NRS include improvements in the quality, integration, and accessibility of RWD, wider use of demonstration projects to highlight the value and applicability of nonrandomized designs, living, and more detailed HTA guidelines, and improvements in HTA infrastructure relating to RWD. CONCLUSION: NRS can represent a crucial source of evidence on treatment effects for use in HTA when RCT evidence is limited.

Estimating the SF-6Dv1 Value Set for a Population-Based Sample in Lebanon.

OBJECTIVES: The SF-6Dv1 is a preference-based measure derived from the SF-36 for use in quality-adjusted life-year estimation for cost-utility analysis. Country-specific value sets for SF-6Dv1 are needed to reflect societal preferences but none are available for Lebanon and other Arabic countries. This study aimed to generate a value set for SF-6Dv1 for Lebanon and to compare results with the UK set. METHODS: A sample of 249 health states defined by the SF-6Dv1 were valued by a representative sample of 577 members of the Lebanon general population, using standard gamble. Several multivariate regression models at mean and individual level were fitted to estimate utilities for all SF-6Dv1 states with selection of best fitting models based on predictive ability, consistency, and model fit. The best fitting models were compared with those fitted in the UK study. RESULTS: Data from 553 eligible respondents providing 3308 valuations were used for the analysis. Lebanese values were consistently higher than UK values, indicating differences in preferences, and there were no negative values. The random effects model using only main effects was the best performing model. There were inconsistencies in 2 dimensions, thereby consistent models were estimated with values ranging from 0.367 to 1. The results are consistent with the UK results. CONCLUSIONS: This study provides the first population-based value set for SF-6Dv1 health states for Lebanon, making it possible to generate quality-adjusted life-years for cost-utility analysis studies. The potential for applications of a standardized utility measure is enormous both in Lebanon and all Arab countries.

Diagnostics and treatments of COVID-19: two-year update to a living systematic review of economic evaluations.

Objectives: As the initial crisis of the COVID-19 pandemic recedes, healthcare decision makers are likely to want to make rational evidence-guided choices between the many interventions now available. We sought to update a systematic review to provide an up-to-date summary of the cost-effectiveness evidence regarding tests for SARS-CoV-2 and treatments for COVID-19. Methods: Key databases, including MEDLINE, EconLit and Embase, were searched on 3 July 2023, 2 years on from the first iteration of this review in July 2021. We also examined health technology assessment (HTA) reports and the citations of included studies and reviews. Peer-reviewed studies reporting full health economic evaluations of tests or treatments in English were included. Studies were quality assessed using an established checklist, and those with very serious limitations were excluded. Data from included studies were extracted into predefined tables. Results: The database search identified 8,287 unique records, of which 54 full texts were reviewed, 28 proceeded for quality assessment, and 15 were included. Three further studies were included through HTA sources and citation checking. Of the 18 studies ultimately included, 17 evaluated treatments including corticosteroids, antivirals and immunotherapies. In most studies, the comparator was standard care. Two studies in lower-income settings evaluated the cost effectiveness of rapid antigen tests and critical care provision. There were 17 modelling analyses and 1 trial-based evaluation. Conclusion: A large number of economic evaluations of interventions for COVID-19 have been published since July 2021. Their findings can help decision makers to prioritise between competing interventions, such as the repurposed antivirals and immunotherapies now available to treat COVID-19. However, some evidence gaps remain present, including head-to-head analyses, disease-specific utility values, and consideration of different disease variants. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021272219], identifier [PROSPERO 2021 CRD42021272219].

A framework for the definition and interpretation of the use of surrogate endpoints in interventional trials.

Background: Interventional trials that evaluate treatment effects using surrogate endpoints have become increasingly common. This paper describes four linked empirical studies and the development of a framework for defining, interpreting and reporting surrogate endpoints in trials. Methods: As part of developing the CONSORT (Consolidated Standards of Reporting Trials) and SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) extensions for randomised trials reporting surrogate endpoints, we undertook a scoping review, e-Delphi study, consensus meeting, and a web survey to examine current definitions and stakeholder (including clinicians, trial investigators, patients and public partners, journal editors, and health technology experts) interpretations of surrogate endpoints as primary outcome measures in trials. Findings: Current surrogate endpoint definitional frameworks are inconsistent and unclear. Surrogate endpoints are used in trials as a substitute of the treatment effects of an intervention on the target outcome(s) of ultimate interest, events measuring how patients feel, function, or survive. Traditionally the consideration of surrogate endpoints in trials has focused on biomarkers (e.g., HDL cholesterol, blood pressure, tumour response), especially in the medical product regulatory setting. Nevertheless, the concept of surrogacy in trials is potentially broader. Intermediate outcomes that include a measure of function or symptoms (e.g., angina frequency, exercise tolerance) can also be used as substitute for target outcomes (e.g., all-cause mortality)-thereby acting as surrogate endpoints. However, we found a lack of consensus among stakeholders on accepting and interpreting intermediate outcomes in trials as surrogate endpoints or target outcomes. In our assessment, patients and health technology assessment experts appeared more likely to consider intermediate outcomes to be surrogate endpoints than clinicians and regulators. Interpretation: There is an urgent need for better understanding and reporting on the use of surrogate endpoints, especially in the setting of interventional trials. We provide a framework for the definition of surrogate endpoints (biomarkers and intermediate outcomes) and target outcomes in trials to improve future reporting and aid stakeholders' interpretation and use of trial surrogate endpoint evidence. Funding: SPIRIT-SURROGATE/CONSORT-SURROGATE project is Medical Research Council Better Research Better Health (MR/V038400/1) funded.

Early Cost-Utility Analysis of Ataluren and Eteplirsen in the Treatment of Duchenne Muscular Dystrophy in Egypt.

OBJECTIVES: Ataluren and eteplirsen are orphan drugs that delay progression of Duchenne muscular dystrophy in mutation-specific subgroups. They have yet to be approved in Egypt but are expected to reach the market soon. This study describes 2 cost-utility models comparing the drugs with the standard of care. METHODS: We used a partition-survival model with 5 states based on the ambulatory status to model a cohort of ambulatory patients at the age of 5 years. Baseline curves were obtained from a published model; then the ambulation loss curve was updated using the Kaplan-Meier curve of the standard of care from a study by McDonald et al. Other curves were updated by calibration to this curve. Costs and utilities were from a local study. Deterministic and probabilistic sensitivity analyses were conducted. Prices were estimated based on other orphan drugs' prices. RESULTS: In the base case, ataluren 1000 mg and eteplirsen 50 mg/mL resulted in an incremental cost-effectiveness ratio of EGP 51 745 605 and EGP 69 652 533/quality-adjusted life-year, respectively, at their hypothetical prices of EGP 308 600 for ataluren 30-sachet pack and EGP 62 800 for eteplirsen 10 mL vial. The incremental cost-effectiveness ratio was sensitive to health state utilities but not to state costs. At EGP 911 719/quality-adjusted life-year threshold, the value-based prices were EGP 4680 for ataluren 1000 mg and EGP 733 for eteplirsen 10 mL vial. CONCLUSIONS: Based on these models, there is a huge gap between the prices of orphan drugs and their value-based prices, which highlights the need for major policy reforms in the assessment and pricing of orphan drugs.

Economic evaluations of artificial intelligence-based healthcare interventions: a systematic literature review of best practices in their conduct and reporting.

Objectives: Health economic evaluations (HEEs) help healthcare decision makers understand the value of new technologies. Artificial intelligence (AI) is increasingly being used in healthcare interventions. We sought to review the conduct and reporting of published HEEs for AI-based health interventions. Methods: We conducted a systematic literature review with a 15-month search window (April 2021 to June 2022) on 17th June 2022 to identify HEEs of AI health interventions and update a previous review. Records were identified from 3 databases (Medline, Embase, and Cochrane Central). Two reviewers screened papers against predefined study selection criteria. Data were extracted from included studies using prespecified data extraction tables. Included studies were quality assessed using the National Institute for Health and Care Excellence (NICE) checklist. Results were synthesized narratively. Results: A total of 21 studies were included. The most common type of AI intervention was automated image analysis (9/21, 43%) mainly used for screening or diagnosis in general medicine and oncology. Nearly all were cost-utility (10/21, 48%) or cost-effectiveness analyses (8/21, 38%) that took a healthcare system or payer perspective. Decision-analytic models were used in 16/21 (76%) studies, mostly Markov models and decision trees. Three (3/16, 19%) used a short-term decision tree followed by a longer-term Markov component. Thirteen studies (13/21, 62%) reported the AI intervention to be cost effective or dominant. Limitations tended to result from the input data, authorship conflicts of interest, and a lack of transparent reporting, especially regarding the AI nature of the intervention. Conclusion: Published HEEs of AI-based health interventions are rapidly increasing in number. Despite the potentially innovative nature of AI, most have used traditional methods like Markov models or decision trees. Most attempted to assess the impact on quality of life to present the cost per QALY gained. However, studies have not been comprehensively reported. Specific reporting standards for the economic evaluation of AI interventions would help improve transparency and promote their usefulness for decision making. This is fundamental for reimbursement decisions, which in turn will generate the necessary data to develop flexible models better suited to capturing the potentially dynamic nature of AI interventions.

A Systematic Literature Review of Gaps and Challenges in Value Assessment of Biosimilars: An ISPOR Special Interest Group Report.

OBJECTIVES: This study aims to provide an overview of the gaps and challenges in the value assessment of biosimilars and to identify potential approaches to address them. METHODS: A multidisciplinary, international team of biosimilar experts identified gaps and challenges. A systematic review was conducted of the peer-reviewed literature in PubMed, EMBASE, Web of Science Core Collection, EBSCOhost Business Source Complete; and of the gray literature. Preliminary results were presented at ISPOR conferences and this article benefited from 2 review rounds among ISPOR Biosimilar Special Interest Group members. RESULTS: Given that a biosimilar is highly similar to its reference biologic, health technology assessment agencies should accept the comparability exercise approved by regulatory authorities and, thus, conduct a price comparison when biosimilar reimbursement is requested for the same indication as the reference biologic. If the reference biologic is not reimbursed or is not the standard of care, a full economic evaluation of the biosimilar versus a relevant comparator needs to be conducted. To date, little consideration has been given to specific challenges, such as how biosimilar value assessment can account for the nocebo effect, potential differences between biologic-naive and biologic-experienced patients, the availability of intravenous and subcutaneous administration forms or different administration devices for the same active compound, value-added services, and the contribution of biosimilars for generating health gain at the population level. CONCLUSIONS: There is a need to gather further insights in the methodology of value assessment for biosimilars, and health technology assessment agencies need to develop more elaborate guidance on biosimilar value assessment in specific circumstances.

Recommendations for patient involvement in health technology assessment in Central and Eastern European countries.

Introduction: Meaningful patient involvement in health technology assessment (HTA) is essential in ensuring that the interests of the affected patient population, their families, and the general public are accurately reflected in coverage and reimbursement decisions. Central and Eastern European (CEE) countries are generally at less advanced stages of implementing HTA, which is particularly true for patient involvement activities. As part of the Horizon2020 HTx project, this research aimed to form recommendations for critical barriers to patient involvement in HTA in CEE countries. Methods: Built on previous research findings on potential barriers, a prioritisation survey was conducted online with CEE stakeholders. Recommendations for prioritised barriers were formed through a face-to-face workshop by CEE stakeholders and HTx experts. Results: A total of 105 stakeholders from 13 CEE countries completed the prioritisation survey and identified 12 of the 22 potential barriers as highly important. The workshop had 36 participants representing 9 CEE countries, and 5 Western European countries coming together to discuss solutions in order to form recommendations based on best practices, real-life experience, and transferability aspects. Stakeholder groups involved in both phases included HTA organisation representatives, payers, patients, caregivers, patient organisation representatives, patient experts, health care providers, academic and non-academic researchers, health care consultants and health technology manufacturers/providers. As a result, 12 recommendations were formed specified to the CEE region's context, but potentially useful for a broader geographic audience. Conclusion: In this paper, we present 12 recommendations for meaningful, systematic, and sustainable patient involvement in HTA in CEE countries. Our hope is that engaging more than a hundred CEE stakeholders in the study helped to spread awareness of the importance and potential of patient involvement and that the resulting recommendations provide tangible steps for the way forward. Future studies shall focus on country-specific case studies of the implemented recommendations.

Definitions, acceptability, limitations, and guidance in the use and reporting of surrogate end points in trials: a scoping review.

OBJECTIVE: To synthesize the current literature on the use of surrogate end points, including definitions, acceptability, and limitations of surrogate end points and guidance for their design/reporting, into trial reporting items. STUDY DESIGN AND SETTING: Literature was identified through searching bibliographic databases (until March 1, 2022) and gray literature sources (until May 27, 2022). Data were thematically analyzed into four categories: (1) definitions, (2) acceptability, (3) limitations and challenges, and (4) guidance, and synthesized into reporting guidance items. RESULTS: After screening, 90 documents were included: 79% (n = 71) had data on definitions, 77% (n = 69) on acceptability, 72% (n = 65) on limitations and challenges, and 61% (n = 55) on guidance. Data were synthesized into 17 potential trial reporting items: explicit statements on the use of surrogate end point(s) and justification for their use (items 1-6); methodological considerations, including whether sample size calculations were informed by surrogate validity (items 7-9); reporting of results for composite outcomes containing a surrogate end point (item 10); discussion and interpretation of findings (items 11-14); plans for confirmatory studies, collecting data on the surrogate end point and target outcome, and data sharing (items 15-16); and informing trial participants about using surrogate end points (item 17). CONCLUSION: The review identified and synthesized items on the use of surrogate end points in trials; these will inform the development of the Standard Protocol Items: Recommendations for Interventional Trials-SURROGATE and Consolidated Standards of Reporting Trials-SURROGATE extensions.

Prognostic Factors of COVID-19: An Umbrella Review Endorsed by the International Society for Pharmacoepidemiology.

During the coronavirus disease 2019 (COVID-19) pandemic, the urgency for updated evidence to inform public health and clinical care placed systematic literature reviews (SLRs) at the cornerstone of research. We aimed to summarize evidence on prognostic factors for COVID-19 outcomes through published SLRs and to critically assess quality elements in the findings' interpretation. An umbrella review was conducted via electronic databases from January 2020 to April 2022. All SLRs (and meta-analyses) in English were considered. Data screening and extraction were conducted by two independent reviewers. AMSTAR 2 tool was used to assess SLR quality. The study was registered with PROSPERO (CRD4202232576). Out of 4,564 publications, 171 SLRs were included of which 3 were umbrella reviews. Our primary analysis included 35 SLRs published in 2022, which incorporated studies since the beginning of the pandemic. Consistent findings showed that, for adults, older age, obesity, heart disease, diabetes, and cancer were more strongly predictive of risk of hospitalization, intensive care unit admission, and mortality due to COVID-19. Male sex was associated with higher risk of short-term adverse outcomes, but female sex was associated with higher risk of long COVID. For children, socioeconomic determinants that may unravel COVID-19 disparities were rarely reported. This review highlights key prognostic factors of COVID-19, which can help clinicians and health officers identify high-risk groups for optimal care. Findings can also help optimize confounding adjustment and patient phenotyping in comparative effectiveness research. A living SLR approach may facilitate dissemination of new findings. This paper is endorsed by the International Society for Pharmacoepidemiology.

Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment.

Background: Artificial intelligence (AI) has attracted much attention because of its enormous potential in healthcare, but uptake has been slow. There are substantial barriers that challenge health technology assessment (HTA) professionals to use AI-generated evidence for decision-making from large real-world databases (e.g., based on claims data). As part of the European Commission-funded HTx H2020 (Next Generation Health Technology Assessment) project, we aimed to put forward recommendations to support healthcare decision-makers in integrating AI into the HTA processes. The barriers, addressed by the paper, are particularly focusing on Central and Eastern European (CEE) countries, where the implementation of HTA and access to health databases lag behind Western European countries. Methods: We constructed a survey to rank the barriers to using AI for HTA purposes, completed by respondents from CEE jurisdictions with expertise in HTA. Using the results, two members of the HTx consortium from CEE developed recommendations on the most critical barriers. Then these recommendations were discussed in a workshop by a wider group of experts, including HTA and reimbursement decision-makers from both CEE countries and Western European countries, and summarized in a consensus report. Results: Recommendations have been developed to address the top 15 barriers in areas of (1) human factor-related barriers, focusing on educating HTA doers and users, establishing collaborations and best practice sharing; (2) regulatory and policy-related barriers, proposing increasing awareness and political commitment and improving the management of sensitive information for AI use; (3) data-related barriers, suggesting enhancing standardization and collaboration with data networks, managing missing and unstructured data, using analytical and statistical approaches to address bias, using quality assessment tools and quality standards, improving reporting, and developing better conditions for the use of data; and (4) technological barriers, suggesting sustainable development of AI infrastructure. Conclusion: In the field of HTA, the great potential of AI to support evidence generation and evaluation has not yet been sufficiently explored and realized. Raising awareness of the intended and unintended consequences of AI-based methods and encouraging political commitment from policymakers is necessary to upgrade the regulatory and infrastructural environment and knowledge base required to integrate AI into HTA-based decision-making processes better.

Health technology assessment of tests for SARS-CoV-2 and treatments for COVID-19: A proposed approach and best-practice recommendations.

OBJECTIVES: To develop best-practice guidance for health technology assessment (HTA) agencies when appraising diagnostic tests for SARS-CoV-2 and treatments for COVID-19. METHODS: We used a policy sandbox approach to develop best-practice guidance for HTA agencies to approach known challenges associated with assessing tests and treatments for COVID-19. The guidance was developed by a multi-stakeholder workshop of twenty-one participants representing HTA agencies, clinical and patient experts, academia, industry, and a payer, from across Europe and North America. The workshop was supported by extensive background work to identify the key challenges, including: targeted reviews of existing COVID-related methods guidance for assessing interventions and clinical guidelines, engagement with clinical experts, a survey and workshop of HTA agencies, a systematic review of published economic evaluations, and a workshop of health economic modelers. RESULTS: We suggest HTA agencies should consider using other types of evidence (e.g., real world) where high-quality randomized controlled trials may be lacking and healthcare systems would value timely HTA outputs. A "living" HTA approach may be useful, given the context of an evolving disease, scientific understanding and evidence base, allowing for decisions to be efficiently revisited in response to new information; particularly, if supported by a common "disease model" for COVID-19. Innovative ways of engaging with the public and clinicians, and early engagement with regulators and payers, are recommended. CONCLUSIONS: HTA agencies should consider the elements of this guidance that are most suited to their existing processes to enable them to assess the effectiveness and value of interventions for COVID-19.

Participating in innovative medicines initiative funded neurodegenerative disorder projects-An impact analysis conducted as part of the NEURONET project.

The European Commission's Innovative Medicines Initiative (IMI) has funded many projects focusing on neurodegenerative disorders (ND) that aimed to improve the diagnosis, prevention, treatment and understanding of NDs. To facilitate collaboration across this project portfolio, the IMI funded the "NEURONET" project between March 2019 and August 2022 with the aim of connecting these projects and promoting synergies, enhancing the visibility of their findings, understanding the impact of the IMI funding and identifying research gaps that warrant more/new funding. The IMI ND portfolio currently includes 20 projects consisting of 270 partner organizations across 25 countries. The NEURONET project conducted an impact analysis to assess the scientific and socio-economic impact of the IMI ND portfolio. This was to better understand the perceived areas of impact from those directly involved in the projects. The impact analysis was conducted in two stages: an initial stage developed the scope of the project, defined the impact indicators and measures to be used. A second stage designed and administered the survey amongst partners from European Federation of Pharmaceutical Industries and Associations (EFPIA) organizations and other partners (hereafter, referred to as "non-EFPIA" organizations). Responses were analyzed according to areas of impact: organizational, economic, capacity building, collaborations and networking, individual, scientific, policy, patient, societal and public health impact. Involvement in the IMI ND projects led to organizational impact, and increased networking, collaboration and partnerships. The key perceived disadvantage to project participation was the administrative burden. These results were true for both EFPIA and non-EFPIA respondents. The impact for individual, policy, patients and public health was less clear with people reporting both high and low impact. Overall, there was broad alignment between EFPIA and non-EFPIA participants' responses apart from for awareness of project assets, as part of scientific impact, which appeared to be slightly higher among non-EFPIA respondents. These results identified clear areas of impact and those that require improvement. Areas to focus on include promoting asset awareness, establishing the impact of the IMI ND projects on research and development, ensuring meaningful patient involvement in these public-private partnership projects and reducing the administrative burden associated with participation in them.

Synergy between health technology assessments and clinical guidelines for multiple sclerosis.

Decision-making for reimbursement and clinical guidelines (CGs) serves different purposes although the decision-criteria and required evidence largely overlap. This study aimed to assess similarities and discrepancies between health technology assessment (HTA) reports as compared to CGs for multiple sclerosis (MS) medicines. All HTA reports and corresponding CGs for MS from the UK, France, Germany, the Netherlands, Poland, Sweden, and the European Union were assessed to identify synergies in recommendations for MS medicines (approved 1995-2020). A content analysis of HTA reports and CGs was performed to identify similarities and discrepancies in wording of treatment recommendations across documents. We assessed 132 HTA reports and 9 CGs for 16 MS treatments. Final recommendations for reimbursement and inclusion in CGs were mostly similar (90%), albeit with considerable differences in treatment lines and subindications. Since 2010, HTA reports refer to the use of CGs in 42% (55/132) and to consultations with clinicians in 43% (57/132) of cases. Six of nine CGs referred to HTA reports and two referred to HTA consultations, in one case having a formal relation to the HTA organization. CGs referenced pharmacoeconomic studies (4/9) for costs and cost-effectiveness. To date, not all new HTA recommendations for MS treatments are included in CGs. Some synergy exists between treatment recommendations in HTA reports and CGs, although discrepancies were seen in timelines and in recommended treatment lines and subindications. More stakeholder dialogue and/or consultation of each other's publications may further improve synergy, facilitate transparency, and enhance patient access.

Guidance on using real-world evidence from Western Europe in Central and Eastern European health policy decision making.

Aim: Real-world data and real-world evidence (RWE) are becoming more important for healthcare decision making and health technology assessment. We aimed to propose solutions to overcome barriers preventing Central and Eastern European (CEE) countries from using RWE generated in Western Europe. Materials & methods: To achieve this, following a scoping review and a webinar, the most important barriers were selected through a survey. A workshop was held with CEE experts to discuss proposed solutions. Results: Based on survey results, we selected the nine most important barriers. Multiple solutions were proposed, for example, the need for a European consensus, and building trust in using RWE. Conclusion: Through collaboration with regional stakeholders, we proposed a list of solutions to overcome barriers on transferring RWE from Western Europe to CEE countries.