Overview
This text aims to provide a robust set of health economic principles and methods for informing societal decisions in relation to research, reimbursement and regulation
Our goal is not to exhaustively cover all methods but to offer a theoretical and practical framework that helps navigate common biases and suboptimal outcomes in health economic analysis, while emphasizing methods designed to tackle these issues effectively.
Our objective is to enhance constrained optimization of community health outcomes while considering societal benefits within budget limitations and available technologies This necessitates the use of efficient methods to inform health system decision-making across research, reimbursement, and regulatory processes Crucially, this approach involves identifying effective strategies to maximize the potential benefits of both existing and new technologies amidst uncertainty.
2010) and associated opportunity costs of adoption and financing actions undertaken with reimbursement and pricing decisions (Pekarsky 2012, 2015; Eckermann and Pekarsky 2014; Eckermann 2015).
Joint coverage and comparability principles (introduced in greater detail in Chap
Assessing incremental costs, effects, and cost-effectiveness, along with determining appropriate threshold values for these effects, is essential for sound and unbiased health economic decision-making This analysis and the methods discussed in this book are crucial for evaluating health interventions effectively.
(i) Cost effectiveness analysis and adoption decisions in Chaps 2, 3 and 4, Part 1; (ii) Joint research and adoption decisions in Chaps 5, 6 and 7, Part 2;
Joint research, along with the adoption and regulation of healthcare providers and systems, should employ a comprehensive multi-strategy approach This method must align with the principles outlined in Chapters 8, 9, and 10 of Part 3, ensuring that the maximum net benefit is achieved across various domains and practices.
Optimizing joint research, reimbursement strategies, and regulations is essential for integrating new technologies with existing options This involves applying health shadow pricing and threshold effect valuation to assess net benefits Such an approach supports budget-constrained decision-making and policy analysis, as discussed in Chapters 11 and 12 of Part 4.
The developed methods for optimizing societal decision-making in health economics emphasize that research and analysis do not occur in isolation By integrating joint societal research with reimbursement and regulatory decisions, these methods facilitate interaction among funders, providers, and manufacturers This approach considers the degree of provider implementation based on the strength of evidence and addresses the incentives shaped by institutional arrangements and policies.
Recognizing the significance of political economy in health system decision-making is crucial, as it emphasizes the need for budget-constrained optimization of net benefits from the community's perspective This approach seeks to mitigate biases that may occur when political and economic factors influence health system decisions, potentially diverting focus from community values to narrower interests, such as those of manufacturers or clinical stakeholders.
2012) As Gavin Mooney often stated, health systems should serve the communities they care for, and central to this is having an underlying societal objective function that reflects community preferences.
An Appropriate Underlying Objective Function
Establishing a clear objective for societal decision-making is crucial in health economics, as it determines what is valued in the principles, evaluation methods, and metrics used for health system decisions This objective should guide all aspects of decision-making, including system architecture, strategies, technologies, and implementation options Health economic evaluations must always define the objective function and consider the perspectives involved Failing to establish an appropriate objective or neglecting community goals can lead to harmful incentives and overly simplistic approaches to complex health issues.
Maximizing the incremental net benefit from a societal decision-making perspective is essential in health technology assessment This approach evaluates the value of incremental effects from various strategies—such as alternative health promotion programs, screening, or diagnostic interventions—by subtracting their incremental costs across the health system Research by Claxton and Posnett (1996), Stinnet and Mullahy (1998), Willan and Lin (2001), Briggs et al (2002), Eckermann (2004), Willan and Briggs (2006), and Drummond et al highlights this metric as a robust and appropriate tool for informing societal decisions in healthcare.
In the realm of investment decisions, Graham (1981, 1992) demonstrated that maximizing net benefit facilitates constrained optimization across both public and private sectors, as long as the threshold values for effects consider the opportunity costs of the best alternative actions Despite this, there are still crucial questions regarding the application of the net benefit metric as a reliable objective function for optimization in healthcare, which are explored in this article.
(i) Is there adequate coverage (scope 1 and duration) as well as comparability to obtain unbiased estimates of incremental costs and effects for robust net ben- efit assessment?
Determining the societal threshold value for net benefit assessment of new and existing health interventions is crucial, especially considering the opportunity costs associated with adopting and financing various investment options This assessment must account for the inefficiencies within the health system and the constraints imposed by limited budgets Establishing an appropriate threshold will help prioritize investments that yield the greatest health benefits while ensuring optimal resource allocation.
(iii) Do efficiency comparisons and funding of health-care providers in practice create incentives consistent with budget-constrained maximising of health sys- tem net benefit of the community?
Cost-effectiveness analysis and health technology assessment processes have largely overlooked coverage issues and biases, focusing predominantly on randomized control trial evidence to mitigate selection bias This narrow emphasis on evidence comparability has inadvertently allowed structural, coverage, and methodological biases to be misinterpreted as uncertainty Consequently, the failure to acknowledge biases beyond selection bias in non-randomized evidence has hindered the proper recognition and control of other biases While the lack of randomization is rightly identified as a selection bias, it is crucial to acknowledge additional biases, as discussed in Chapters 2 and 3.
Evaluating palliative care requires comprehensive methods to compare various outcomes, including the impacts on carers, families, and patients during the dying process, such as finalizing affairs and preferred dying settings Traditional metrics like quality-adjusted life years (QALYs) fall short, as they cannot encapsulate these diverse domains and often suffer from subjectivity and lack of universal applicability Therefore, it is essential to adopt approaches that facilitate the comparison of multiple outcome domains, as discussed in Chapters 4 and 10, which emphasize robust methods for analyzing diverse strategies and outcomes on the cost-disutility plane.
(i) Inadequate or inconsistent coverage of the scope and/or duration of incremen- tal effects and costs (O’Brien 1996);
(ii) Partial analysis of cost and effects such as the box method (Briggs et al 2002) and cost minimisation analysis (Briggs and O’Brien 2001); and
Selection biases can impact indirect comparisons and the translation of evidence when using relative risk, a nonsymmetric metric Inconsistencies often emerge from different framings of binary outcomes, such as survival versus no survival or progression versus no progression, particularly when synthesizing or translating evidence (Eckermann et al 2009, 2011).
Addressing biases before assessing uncertainty is essential for informed societal decision-making, as modeling uncertainty based on biased estimates can lead to misleading conclusions and exploitation by vested interests Evaluating cost and effectiveness without first establishing unbiased estimates is like wearing rose-colored glasses, preventing a clear view of reality.
The Arrow-Lind theorem emphasizes the necessity of eliminating biases when addressing uncertainty in public investment decisions It illustrates that societal decision-making, characterized by risk spreading and diversification, should prioritize expected cost-effectiveness, especially as preferences trend towards risk neutrality This concept is crucial for comparing multiple strategies and understanding the various decisions made over time by governmental jurisdictions and their regulatory bodies, such as the PBAC and NICE, affecting large populations.
Part 1 of the article underscores the importance of methods that ensure joint satisfaction of coverage and comparability principles while minimizing biases, which are essential for enhancing societal decision-making Chapter 2 discusses the necessity of considering both costs and effects in within-trial cost-effectiveness analysis, marking a departure from outdated cost minimization approaches Chapter 3 addresses common issues where selection biases may unintentionally arise during evidence synthesis, extrapolation, and application to relevant jurisdictions Crucially, the chapter identifies solutions that facilitate unbiased and consistent estimates of cost-effectiveness, thereby supporting informed decision-making.
Chapter 4 explores the complexities of community-based settings in health promotion and palliative care, emphasizing the network multiplier effects and the importance of comparing multiple domains It highlights the principles of coverage and comparability when evaluating population network impacts over time These concepts are further illustrated in Chapter 12, which focuses on community-ageing policies, and Chapter 10, which delves into multiple domain comparisons.
Coverage and comparability principles, along with methods to mitigate bias, form the foundation for effective and efficient cost analysis that guides reimbursement decisions in health technology or program assessments across various jurisdictions These principles are emphasized throughout the text to ensure informed decision-making in related areas.
(i) Joint research and reimbursement locally and globally with value of informa- tion methods illustrated in Part II (Chaps 5, 6 and 7) which avoid partial hypothesis test problems of conventional methods.
In Part III (Chapters 8, 9, and 10), the article compares multiple strategies, providers, and outcomes using analysis methods and summary measures that effectively mitigate inferential and conflation issues Part IV (Chapters 11 and 12) discusses the regulation of budget-constrained threshold values, pricing, and system efficiency, along with the associated research, reimbursement decisions, and policy challenges It emphasizes the need to navigate political economy obstacles to achieve optimization while avoiding silo mentalities in both policy and practice.
Health promotion and prevention programs in complex community settings, such as schools, face challenges in satisfying coverage and comparability principles However, these settings also offer significant potential for cost-effective expansion of health promotion effects due to community ownership and network impacts To ensure the success of these strategies, it is essential to evaluate their acceptance, long-term integration, and ownership within targeted communities, alongside their influence on individual behaviors The impacts of community behavioral change often extend beyond short-term evaluations, affecting health and generating diffuse network-related effects across populations These long-term community-level impacts are vital for assessing the effectiveness and cost-effectiveness of health promotion strategies, yet traditional evaluation methods focused on individuals fail to capture them adequately Consequently, conventional cost-effectiveness analysis models that rely solely on patient-level evidence struggle to accurately estimate the long-term societal costs and benefits of health promotion and prevention initiatives.
Measuring the network and multiplier effects of initial investments in community activities provides valuable quantitative indicators of community ownership, engagement, social networks, and the sustainability of health programs over time (Hawe et al 2009; Shiell et al 2008) These multiplier impacts can be effectively triangulated with qualitative assessments, highlighting the need for different evaluation approaches for community health promotion and prevention programs compared to individual-level therapies This concept is exemplified in the evaluation of a kitchen-garden health promotion program in primary schools (Eckermann et al 2014).
Principles for Constrained Optimisation Across Health Promotion, Prevention and Care Settings
To facilitate cost-effective optimization in health prevention and promotion, as well as in diagnostic, curative, rehabilitative, and palliative care, health economics must employ strong principles and adaptable methods These approaches should provide unbiased insights to guide societal decision-making in joint research, reimbursement, and regulatory processes.
In identifying robust and principled health economic methods for constrained maximisation across these health care setting and joint decisions, we bring together:
The groundbreaking work of Bernie O’Brien and his colleagues, including Andy Willan and Andy Briggs, emphasizes the importance of integrating both clinical and economic factors to evaluate costs and outcomes collectively (O’Brien 1996; Briggs and O’Brien 2001; Briggs et al 2002; Willan and Briggs 2006) Furthermore, the application of decision analytic principles regarding coverage and comparability is essential to prevent biases and inferential errors in evidence synthesis, translation, and extrapolation, ultimately guiding informed societal decision-making across relevant jurisdictions (Eckermann et al 2009, 2011).
Robust evaluation methods for health promotion strategies in community settings are essential Following the research of Shiell and Hawe, it is crucial to consider community-level social capital and the network multiplier effects of these strategies in practice Understanding these factors can significantly enhance the effectiveness of health promotion initiatives.
(iv) Value of information methods enabling optimisation of joint research and reimbursement decisions allowing for key decision contexts (Eckermann and Willan 2007, 2008a, b, 2009, 2011, 2013; Eckermann et al 2010; Willan and Eckermann 2010, 2012).
Effective regulation methods are essential for creating economic incentives that maximize net benefits while ensuring efficiency among multiple providers and strategies This includes making comparisons across various outcomes Additionally, it is crucial to establish budget-constrained threshold values that account for the opportunity costs associated with adopting and financing new technologies These values should consider alternative research and reimbursement options relevant to the specific decision contexts within any jurisdiction, emphasizing the significance of health shadow price research.
The Health Economics from Theory to Practice course provides a comprehensive framework to address key areas in health systems, emphasizing the importance of aligning community objectives and values with evidence-based societal decision-making Central to this framework is the political economy of health systems, which influences institutions, decision-making, and actions critical for optimizing societal outcomes Given the unique nature of healthcare transactions, understanding community values is essential for achieving equity and efficiency Additionally, inherent asymmetries of information between providers and patients, coupled with patients' bounded rationality in complex decision-making contexts, highlight the challenges faced in healthcare agency relationships, which are common across various health systems.
In healthcare settings, significant information asymmetries exist between providers and patients, both before and after treatment, which complicates decision-making processes These disparities necessitate that healthcare providers act as patient agents to facilitate efficient choices However, they also create conditions for supplier-induced demand, leading to potential overtreatment and negative health outcomes Understanding these dynamics is essential for developing effective policy and regulatory frameworks that promote efficiency and equity in healthcare institutions The principles of health economics underscore the importance of universal public healthcare provision, emphasizing equity, population health, and the need for appropriate incentives for providers to enhance system efficiency.
Evidence strongly supports the necessity of universal health care and effective payment structures that incentivize appropriate care, as opposed to creating perverse incentives that lead to unnecessary demand This is crucial for enhancing both efficiency and equitable access within health systems A comparison of the US health system with universal access models in countries like Canada, the UK, France, and Australia reveals significant disparities in costs and outcomes Notably, in 2013, the US health system incurred costs that were, on average, double the percentage of GDP spent by universally publicly funded systems in OECD countries.
Despite spending $8,505 per capita on healthcare, the United States lacks universal access and exhibits some of the poorest health outcomes among OECD countries, with life expectancy in 2013 lower than any nation spending over $2,000 per capita This inefficiency is characterized by higher costs, worse health results, and significant inequities, as the highest income quintiles receive excessive services while those with limited access are underserved The over-servicing is exacerbated by defensive medicine practices driven by litigation fears, leading to unnecessary tests and treatments, particularly for rare conditions, as well as the problematic use of polypharmacy to manage side effects.
The high costs of healthcare in the USA can be attributed to the complexity of a non-universal access system, which necessitates extensive monitoring of access and exclusion criteria across various care provisions, such as private insurance, Medicare, and Medicaid Approximately 25% of the US healthcare system's expenses are linked to administrative tasks, in stark contrast to the 10% seen in universal healthcare systems Additionally, private insurance holders often face over-servicing when treatment is accessible, while simultaneously experiencing treatment denials for pre-existing conditions, a challenge exacerbated by the need for extensive administrative processes to identify these conditions This administrative burden not only increases costs for both patients and insurers but also negatively impacts health outcomes by delaying necessary treatments.
Publicly funded universal health systems are generally more cost-effective and provide better access and health outcomes compared to privately funded systems, as supported by empirical evidence (OECD 2013; Davis et al 2014) However, their efficiency and equity benefits hinge on the systems offering suitable incentives for providers and aligning with community goals To maximize community benefits within limited budgets, decision-making must reflect these objectives and consider the opportunity costs involved This is crucial for evaluating strategies related to existing and emerging technologies and ensuring effective coordination across the health system over time This article addresses key health economic issues, highlighting common biases and challenges associated with fragmented approaches while proposing straightforward methods to enhance research, reimbursement, and regulatory decisions.
This article explores the integration of Western evidence-based medicine with Eastern preventative health approaches, aiming to address a comprehensive range of health options It highlights the limitations of health technology assessment (HTA) systems that prioritize patentable medications and devices, which create barriers to researching and adopting non-patentable alternatives This oversight hinders the evaluation of existing programs and community health initiatives, such as rehabilitation and prevention strategies By advocating for improved information flow and care coordination, the article emphasizes the need to overcome obstacles and enhance the implementation of effective health strategies, ensuring that both patentable and non-patentable options are appropriately compared and utilized.
(i) Expanding use of ‘off-patent’ medication and its better use in indicated popu- lations, e.g use of existing statins.
Non-patentable alternative modalities, including rehabilitative care for coronary heart disease (CHD) and chronic obstructive pulmonary disease (COPD), as well as palliative care support in home or institutional settings, provide viable alternatives to conventional therapies like radiotherapy and chemotherapy for cancer patients.
Community-based approaches to health promotion and primary prevention, such as community gardens and school kitchens, play a crucial role in enhancing public health These initiatives, along with walking paths and age- and dementia-friendly facilities, programs, and policies, contribute significantly to creating healthier environments (Eckermann et al 2014; Kalache 2013).
The application of natural plant varieties and extracts at factor costs is increasingly recognized for treating common conditions Notably, medicinal cannabis harnesses the entourage effects of CHD-, terpene-, and THC-rich strains, tailored to meet individual patient needs and tolerance levels This approach is particularly beneficial in palliative pain management, as supported by various studies (Wagner and Ulrich-Merzenich 2009; Russo 2011; Gallily et al 2015; Johnson et al 2010; Carter 2011).
Chapter 12 delves into various policy, research, reimbursement, pricing, and practice options to address the challenges posed by the aging baby boomer population within the health and aged care systems Traditional evidence-based medicine (EBM) and health technology assessment (HTA) methods often fail to optimize health outcomes under budget constraints, particularly when non-patentable options are not adequately explored alongside patentable technologies This oversight can create institutional barriers, leading to selection bias that favors funding for new patentable technologies over potentially effective alternatives Consequently, this results in poorly informed societal decisions regarding the reimbursement and financing of new technologies, neglecting the opportunity costs associated with existing programs A comprehensive comparison with the most cost-effective expansions of current programs and the contraction of less effective ones is essential for informed decision-making, as emphasized by Pekarsky's research and further discussed in Chapter 11.
Overview of Chapters
This chapter highlights key aspects of health economic analysis, focusing on the constrained optimization of societal decision-making objectives It emphasizes the importance of incorporating community values to ensure principled and robust evaluations across various contexts, including technology, programs, policies, and practices in health promotion, prevention, treatment, rehabilitation, and palliative care.
Robust problem definition (PICO) & principles for unbiased CE analysis - opportunity cost, coverage & comparability (Chap 1, 2)
Further research locally, or globally with risk sharing (Chap 7) in jurisdictions who AT
ENG positive locally/globally at health shadow price/s
Locally - Delay and Trial (DT)
Globally – DT or Adopt and Trial (AT) with evidence translation & risk-sharing option
Negative ENG for all designs while positive INB at given price - sufficient evidence, Adopt Now (AN)
& translation (Chap 3, 4) to estimate incremental
E, C & NB for any given jurisdiction (Chap 8–10 for multiple strategy/ domains) at their relevant health shadow price (Chap 11)
Value of information analysis locally and/or globally (Chap 5, 6) ENG of further research given price?
Regulate to create incentives consistent with maximising NB in practice (Chap 9–12)
- Reject in favour of alternative optimal adoption and financing options, unless price reduced for expected positive INB
Expected positive while uncertain INB
Fig 1.1 Optimal decision making cycles for joint research, reimbursement and regulatory processes locally and globally
Chapter 2 emphasizes the importance of coverage and comparability principles as essential for unbiased decision-making in health economic analysis It explores effective methods to enhance cost-effectiveness analysis and adoption decisions, highlighting that adherence to these principles is crucial for minimizing biases The chapter illustrates that a comprehensive evaluation of the scope and duration of downstream costs and health effects is necessary when comparing strategies and assessing relative treatment effects.
The net benefit metric offers significant advantages over incremental cost-effectiveness ratios (ICERs) by effectively summarizing cost-effectiveness evidence, especially in the context of decision uncertainty Key presentation tools, such as the incremental cost-effectiveness plane and cost-effectiveness acceptability curves, are introduced for trial-based analysis, demonstrating their simplicity in accommodating joint cost and effect distributions through non-parametric bootstrapping and parametric methods like Fieller’s The importance of jointly considering costs and effects is emphasized to prevent bias and inferential fallacies in decision-making under uncertainty, as discussed in pivotal works like "The Death of Cost Minimisation" by Briggs and O’Brien (2001) and "Thinking Outside the Box" by Briggs et al (2002), which also highlight broader issues of bias associated with reductionist approaches.
(i) Chapter 3 for modelled cost effectiveness analysis;
(ii) Chapters 5, 6 and 7 for value of information (VOI) analysis;
(iii) Chapters 4, 8 and 10 for multiple strategy and outcome comparisons;
(iv) Chapter 9 in efficiency measurement across providers in practice consistent with maximising net benefit; and
(v) Chapters 11 and 12 in appropriately considering alternative actions for identi- fying the opportunity costs of investing in, and pricing of, new technology.
Chapter 3 discusses significant issues and risks associated with biased methods in modeled cost-effectiveness analysis, particularly when coverage and comparability principles are compromised It emphasizes the problems that arise from the choice of methods and metrics used in synthesizing, translating, and extrapolating evidence The chapter illustrates these concerns through examples of inferential fallacies and inconsistencies, particularly related to the use of relative risk in indirect comparisons and evidence translation, as highlighted by Eckermann et al (2009).
In health economic evaluation, parametric methods highlight challenges in extrapolating costs, effects, and cost-effectiveness due to factors like compliance, resistance, and side effects To address these issues, odds ratio methods provide unbiased estimates through alternative outcome framing in indirect comparisons Additionally, decision analytic modeling approaches that account for treatment effects based on indication, continuation rules, and compliance in actual patient populations enable consistent extrapolation of costs and effects These solutions underscore the importance of integrating trial-based and model-based evaluations to generate evidence relevant to decision-making bodies, such as the Pharmaceutical Benefit Advisory Committee (PBAC) in Australia.
‘Frankenstein’s Monster or the Vampire of Trials’ (O’Brien 1996) takes centre stage.
To ensure unbiased assessments of cost-effectiveness under uncertainty, it is essential to integrate coverage and comparability principles Chapter 3 demonstrates these principles through a two-strategy comparison in modelled analysis, while Chapter 8 expands on robust presentation and summary measures for comparisons involving multiple strategies and outcomes.
In addressing the challenges of prevention and health promotion strategies in complex community settings, it is crucial to consider the issues of partialisation and the failure to reflect community values Chapter 4 delves into the difficulties encountered in health economic analysis when comparing various strategies, particularly in environments like schools and palliative care, where multiple domains must be evaluated Traditional cost-effectiveness methods struggle to capture community acceptance and the diffusion of impacts over time, highlighting the need for alternative evaluation approaches Research by Shiell and Hawe emphasizes the importance of assessing network multiplier impacts from community investments, providing a more robust framework for understanding the long-term success of health promotion initiatives The application of multiplier methods is exemplified in the evaluation of the Stephanie Alexander Kitchen Garden National Program, showcasing their effectiveness in assessing complex interventions in primary schools.
McCaffrey et al (2010, 2013, 2015) emphasize the importance of comparing multiple outcome domains under uncertainty, as discussed in Chapter 4 and elaborated in Chapter 10 These comparisons are particularly beneficial in contexts like palliative care, where factors such as finalizing affairs and the process of death cannot be integrated with survival time, thus challenging traditional quality-adjusted life year (QALY) metrics The ability to present multiple events or effects contributing to QALY estimates enhances decision-making by allowing for a robust analysis of joint uncertainty This approach recognizes that baseline risk and utility weights can vary across different populations, jurisdictions, and over time, ultimately enriching the understanding of community utility functions beyond single health metrics.
In Chapter 5, the relationship between optimal decision-making for evidence-based reimbursement of technologies and research decisions is explored, emphasizing the importance of incremental cost-effectiveness under uncertainty Traditional frequentist trial design methods, which rely on type I and type II errors and minimum significant differences, fail to account for the expected value or cost of information, limiting their effectiveness in trial design and decision-making In contrast, Bayesian methods facilitate the joint optimization of research and reimbursement decisions, providing robust estimations of expected value and research costs tailored to specific decision contexts and accounting for prior uncertainties in incremental net benefits, as well as trial size and design.
To accurately assess the distribution of Incremental Net Benefits (INB) and conduct a valuable Value of Information (VOI) analysis in any jurisdiction, it is essential to obtain unbiased estimates of incremental costs and effects, as outlined in Chapters 2, 3, and 4 Additionally, establishing a meaningful threshold value for effects is crucial Therefore, these chapters should be considered in conjunction with Chapter 11 to derive a robust estimate of the INB distribution relevant to local decision-making contexts before proceeding with the VOI analysis discussed in subsequent chapters.
An unbiased estimate of the expected Incremental Net Benefit (INB) is essential for guiding societal decision-making, as outlined in the Arrow-Lind theorem This estimation not only provides crucial information for decision-makers when evaluating reimbursement options, but also helps identify the tail distribution, which is vital for calculating the expected value of sample information (EVSI) Additionally, it highlights the opportunity costs associated with postponing adoption while further research is conducted.
Chapter 5 identifies and illustrates the principles and methods of Value of Information (VoI) that optimize the expected net gain from local trial design and decision-making Notably, the CLT-based VoI methods discussed demonstrate significant effectiveness in enhancing decision-making processes.
(i) Simply applied in estimating expected value of actual trial designs (expected value of sample information) given estimates of mean cost and effects of their variance and covariance; and
Jurisdictions must consider relevant decision contexts when estimating expected value and costs for local decision-making Key factors include recruitment rates, follow-up and analysis time, as well as the opportunity cost and potential option value associated with delays and imperfect implementation.
The CLT methods effectively adhere to Occam's Razor in relation to VOI methods, as demonstrated by Eckermann et al (2010) This allows for straightforward optimization of ENG within relevant decision contexts, offering the essential conditions needed to inform decisions locally.
(i) Is further research for a specific HTA potentially worthwhile?
(ii) Is a given research design worthwhile?
(iii) What is the optimal research design?
(iv) How can funding best be prioritised across alternative research proposals?
Optimizing ENG is essential for guiding research grant allocation bodies, particularly those focused on maximizing the 'value for research dollar.' By considering key decision contexts, this approach can enhance the effectiveness of funding strategies and improve research outcomes.