Disease-modifying dementia treatments (DMDTs) target amyloid beta or tau proteins and have the potential to change disease progression, representing a step change in the management and treatment of Alzheimer’s disease. Given the novel mechanism of action and impact on health care, the NICE Health Technology Assessment Innovation Laboratory (HTA Lab) sought to identify and contextualize the key issues for future appraisals.

We reviewed published assessment reports of DMDTs from international HTA agencies and conducted a scoping review of published economic models of pharmacological treatments to understand the challenges associated with evaluating the cost-effectiveness of dementia treatments. The HTA Lab held an engagement workshop with 27 external stakeholders, including expert clinicians, implementation partners, health economists, and representatives from international agencies to discuss and confirm the key issues and considerations likely to emerge during an appraisal of DMDTs.

Key clinical and cost-effectiveness issues were identified and discussed. We concluded that consideration needs to be given to the diagnostic methods to identify the DMDT-eligible population in the UK, the validity of the surrogate outcomes used in the DMDT clinical trials, treatment effectiveness in different populations, and the incidence of DMDT-associated adverse events. Economic considerations include the type of economic model used in the appraisal, modeling the natural history of the disease, paucity of quality-of-life data in the treatment population, the inclusion of societal impact, treatment duration, stopping rules, and long-term effectiveness beyond the clinical trials.

DMDTs could have the potential to transform Alzheimer’s disease care. With multiple treatments on the horizon, the appropriateness and acceptability of the new mechanism of action underpinning these treatments should be considered. We have identified areas of uncertainty that are likely to arise during an appraisal process, to facilitate the timely approval of these medicines and patient access.

Target product profiles (TPPs) can be used to outline the minimal and desirable characteristics of a health technology to meet a particular health need. They have been used by industry, regulators, and international health agencies to guide product development and facilitate communication across stakeholders, but there appears to have been limited consideration of their utility within HTA.

We completed a narrative review of academic and grey literature on uses of TPPs across the product development, regulatory, and HTA pathway and considered areas within HTA where TPPs could have a role. We then developed a case study alongside an assessment of virtual ward platforms for acute respiratory infection. This case study aimed to explore whether it was possible to construct a TPP to support guidance based on HTA committee discussions and to consider associated opportunities and challenges. We also compared our TPP with other product specifications used within NHS England to support commissioning.

There has been discussion about how industry can build HTA perspectives into TPPs. However, we did not identify use of TPPs led by HTA agencies. Two use cases of TPPs were identified: (i) to support demand signaling for areas of unmet need and (ii) to provide a framework for class-based recommendations. HTA committee discussions supported the development of a TPP for virtual ward platforms with domains relating to patient population, technological specification, evidence requirements, and other factors (e.g., patient choice). Challenges around the time needed to consider both evidence and desired attributes and overlap with approaches used in commissioning were identified.

TPPs may have a role to play within HTA to highlight areas of unmet need or to support innovative approaches to guidance. Digital health technologies may be particularly amenable to these approaches due to the larger range of similar products and ease of replicability. Further work should define how TPPs can be used to their full potential.

There is an increasing number of policy and guidance documents on the use and acceptability of real-world evidence (RWE) to support regulatory and health technology assessment (HTA) decision-making. The Innovative Health Initiative Integration of Heterogeneous Data and Evidence towards Regulatory and HTA Acceptance (IDERHA) partnership is undertaking a global landscape review of these documents to understand where there is consensus and divergence, and where further policy development is needed.

A literature search of the MEDLINE and Embase databases was performed, in addition to handsearching the websites of specific HTA and regulatory organizations. All policies, standards, frameworks, and guidance documents on requirements for acceptable RWE data use published from 2017 were included. Two reviewers independently extracted data using a standard data extraction form that was pilot tested before use. Any discrepancies between the reviewers were resolved by consensus. Extracted data are currently being analyzed by researchers with regulatory or HTA expertise. A workshop held in October 2023 sought input from experts on analysis plans.

The initial literature search yielded 3,184 results. After screening against the inclusion criteria, a total of 87 documents were selected for full-text review (21 HTA and 62 regulatory documents). Of these, 32 were identified as key documents and prioritized for initial review. Key themes in the documents, including transparency, data collection, study design, and data quality, were identified and validated in a workshop with five regulatory or HTA experts. Data extraction is ongoing for the remaining documents and any further themes identified will be added. Any gaps and areas of divergence will be identified, so they can be addressed by future IDERHA work.

This review assessed the increasingly complex global landscape of regulatory and HTA policies and guidance on the use of RWE. Through the exploration of similarities, differences, and gaps in these policies, this work will extend the current understanding of best practice and identify areas that need development of further guidance.

Progress and innovation in artificial intelligence (AI)-based healthcare interventions continue to develop rapidly. However, there are limitations in the published health economic evaluations (HEEs) of AI interventions, including limited reporting on characteristics and development of algorithms. We developed an extension to the existing Consolidated Health Economic Evaluation Reporting Standards (CHEERS) to improve consistency, transparency, and reliability of the reporting of HEEs of AI interventions.

The Delphi method was used, following a prespecified study protocol. A steering group with expert oversight was formed to guide the development process. A long list of potential items was defined based on two recent systematic reviews of HEEs of AI-based interventions. The steering group identified and invited 119 experts to the three-stage survey. Participants were asked to score each item on a nine-point Likert scale, and they were also able to provide free-text comments. The final checklist was piloted on a random sample of nine HEEs of AI-based interventions.

Three stages of the Delphi survey were completed by 58, 42, and 31 multidisciplinary respondents, respectively, including HTA specialists, health economists, AI experts, and patient representatives. The CHEERS-AI extension includes 18 AI-specific reporting items. Ten are entirely new items, including considerations about user autonomy, validation of the AI component, and AI-specific uncertainty. In addition, elaborations on eight existing CHEERS items were added to emphasize important AI-specific nuances. Some participants highlighted that CHEERS-AI can provide key benefits; for example, it could clarify the misconception that the predictive algorithms supporting AI-driven healthcare interventions are available for use without cost.

CHEERS-AI can aid in improved reporting quality for researchers, editors, and reviewers conducting or assessing HEEs of AI interventions.

The COVID-19 pandemic put substantial strain on healthcare systems globally. Early decision-making about diagnostic tests and treatments was driven by the need for rapid responses with a focus on reducing clinical burden. As COVID-19 continues its transition into an endemic state, health technology assessment (HTA) agencies will need to consider the clinical- and cost-effectiveness of tests and treatments, as with other conditions.

We first conducted a systematic literature review in July 2021 and updated the search in July 2023. The review aimed to identify economic evaluations of diagnostics for SARS-CoV-2 and treatments for COVID-19 using predefined search strategy across journal databases and sources of grey literature. In the update, an additional targeted search was completed with terms relating to novel treatments. Search results were screened by title and abstract, and full texts of potentially relevant studies were reviewed against selection criteria. Studies with very serious methodological limitations were excluded. Findings from studies were synthesized narratively due to high levels of heterogeneity.

The database search identified 8,287 unique records, of which 54 full texts were reviewed, 28 were quality assessed, and 15 were included. Three further studies were included through HTA sources and citation checking. Of the 18 studies ultimately included, 16 evaluated pharmacological treatments including corticosteroids, antivirals, and immunotherapies. Two studies in lower-income settings evaluated the cost-effectiveness of rapid antigen tests and critical care provision. In most studies, a healthcare or payer perspective was used, and the comparator was standard care. There were 17 modeling analyses and one trial-based evaluation. Cost–utility analyses using QALYs were the most common analysis type.

This update indicates that there are cost-effective treatments for COVID-19, with repurposed pharmacological treatments like dexamethasone presenting best value. There also appear to be promising options for people with severe disease alongside standard care. Future economic evaluations would benefit from reflecting the changing context around COVID-19 with parameters that reflect current circumstances, and fully incremental analyses comparing different treatment options.

The sandbox approach, developed in the financial technologies sector, creates an environment to collaboratively develop and test innovative new products, methods and regulatory approaches, separated from business as usual. It has been used in health care to encourage innovation in response to emerging challenges, but, until recently, has not been used in health technology assessment (HTA). This article summarizes our learnings from using the sandbox approach to address three challenges facing HTA organizations and to identify implications for the use of this approach in HTA.

We identified three challenging contemporary HTA-related topics to explore in a sandbox environment, away from the pressures and interests of “live” assessments. We convened a pool of 120 stakeholders and experts to participate in various sandbox activities and ultimately co-develop solutions to help HTA organizations respond to the identified challenges.

Important general learnings about the potential benefits and implementation of a sandbox approach in HTA were identified. Consequently, we developed recommendations to guide its use, including how to implement an HTA sandbox in an effective way and the types of challenges for which it may be best suited.

For many HTA organizations, it is difficult to carefully consider emerging challenges and innovate their processes due to risks associated with decision errors and resource limitations. The sandbox approach could reduce these barriers. The potential benefits of addressing HTA challenges in a collaborative “safe space” are considerable.

To develop best-practice guidance for health technology assessment (HTA) agencies when appraising diagnostic tests for SARS-CoV-2 and treatments for COVID-19.

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.

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.

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.

The SF-6D is a preference-based measure of health, derived from the SF-36 for economic evaluation. No value set exists for the SF-6D in Lebanon and other Arabic speaking countries in the Middle East. The aim of this study was to examine the feasibility and acceptability of using the standard gamble (SG) technique to generate preference-based values for the Arabic version of SF-6D in a Lebanese population.

The SF-6D was translated into Arabic using forward and backward translations. Forty-nine states defined by the SF-6D were selected using an orthogonal design and grouped into seven sets. A gender-occupation stratified sample of 126 Lebanese adults from the American University of Beirut were recruited to value seven states and the pits (worst) SF-6D health states using SG. Mean and individual level multivariate regression models were fitted to estimate preference weights for all SF-6D states. The quality of data and the predictive power of the models were compared with results from the United Kingdom (UK).

All respondents completed the interviews with 25 percent reporting that the SG task was difficult and 21 percent felt some degree of irritation or boredom. A total of 992 (98% out of 1,008 observations) SG valuations were useable for econometric modeling. There was no significant change in the test–retest values of 21 subjects. The mean absolute errors in the mean and individual level models were 0.036 and 0.050, respectively, both of which were lower than the UK results. The random effects model adequately predicts the SG values, with the worst state having a value of 0.322 compared to 0.271 in the UK.

This study confirmed that it was feasible and acceptable to generate preference values with the SG method for the Arabic SF-6D in a Lebanese population. This would be the first step towards developing SF-6D value set for Lebanon to be used in economic evaluation studies and to support resources allocation decisions.

Reimbursement decision making is based on a relative effectiveness assessment (REA), which may be combined with a cost-effectiveness assessment, by national Health Technology Assessment (HTA) agencies. These assessments are based on clinical data where new interventions are compared to the current standard of care, which may differ between countries. Since most pivotal trials only include a limited number of interventions, indirect treatment comparisons (ITCs) can be used to compare multiple interventions. The aim of this study was to evaluate the use of ITCs in HTA decision making in the Netherlands and England.

All pharmaceutical assessments published between 2015 and 2019 by the National Health Care Institute (ZIN) and the National Institute for Health and Care Excellence (NICE) were reviewed to determine whether an ITC had been used. For detailed analysis we included all assessments of ZIN using an ITC, and a random sample of assessments of NICE using an ITC (10 assessments per publication year).

Between 2015 and 2019 a total of 106 and 265 assessments were conducted by ZIN and NICE, respectively. Of these assessments 48 from ZIN and 150 from NICE included an ITC. The detailed analysis showed that pharmaceutical assessments including indirect comparative evidence led to the REA conclusion of similar therapeutic evidence in 57 percent of 48 assessments by ZIN and in 52 percent of 50 assessments by NICE. Reimbursement recommendations including indirect comparative evidence most often resulted in positive recommendations by ZIN (57% assessments), and in restricted recommendations by NICE (50% assessments). Different methods were employed to incorporate indirect comparative evidence, such as naïve ITCs and network meta-analysis.

Our results showed a significant variability in the use of ITCs between NICE and ZIN, which may contribute to differences in their recommendations. Further analysis will provide deeper insight in these differences and may provide suggestions for a clearer international guidance on the use of ITCs for HTA.

There are many challenges encountered in the health technology assessment (HTA) of treatments for the management of neurodegenerative diseases like Alzheimer’s disease. A recent example where such challenges were visible was the assessment of aducanumab, a medication developed for Alzheimer’s disease. As part of the Innovative Medicines Initiative (IMI) Neuronet project, a series of workshops were held to understand the gaps in neurodegenerative disease research and identify the key research priorities, from the perspective of different stakeholders, that can inform decision-making and enable timely access to effective and safe treatments.

Three workshops were organised by Neuronet to consult with HTA and regulatory experts, people with dementia and their supporters, and academic and industry researchers. The first consultation was with the European Working Group of People with Dementia and their supporters, and their feedback was fed into the following two workshops which were conducted with HTA and regulatory experts, and with researchers. Discussions during the workshops were used to develop a summary of the gaps in research, and priorities from the perspective of each stakeholder group to be submitted to IMI to inform future research funding allocation.

Key priorities that were highlighted from an HTA and regulatory perspective were the collection of longitudinal data to understand the natural history of the disease, the generation of data on long-term clinical outcomes and the use of trial endpoints that are relevant to clinical practice. There is also a need for disease-modifying treatments that is yet to be met in this disease area. Furthermore, the involvement of the public, particularly people with dementia and relevant organisations, in setting research priorities and informing study designs was highlighted as a priority by all stakeholders.

There are key priorities for future neurodegenerative research from an HTA and regulatory perspective to address current gaps and challenges. It is important to involve different stakeholders in priority setting and decision-making to produce relevant and satisfactory research outputs.