Early Health Economic Modelling Research Articles

MT69 Early Health Economic Modelling to Support the Development of Lifechamps Digital Platform for Older Cancer Survivors

MT69 Early Health Economic Modelling to Support the Development of Lifechamps Digital Platform for Older Cancer Survivors

HTA96 Applying Early Health Technology Assessments to Inform Novel Health Technology Innovation

To examine how early health economic models and technology assessments can be used to inform and influence the development of novel health technologies.

The potential value of disease-modifying therapy in patients with spinocerebellar ataxia type 1: an early health economic modeling study

ObjectiveThere currently is no disease-modifying therapy for spinocerebellar ataxia type 1 (SCA1). Genetic interventions, such as RNA-based therapies, are being developed but those currently available are very expensive. Early evaluation of costs and benefits is, therefore, crucial. By developing a health economic model, we aimed to provide first insights into the potential cost-effectiveness of RNA-based therapies for SCA1 in the Netherlands.MethodsWe simulated disease progression of individuals with SCA1 using a patient-level state-transition model. Five hypothetical treatment strategies with different start and endpoints and level of effectiveness (5–50% reduction in disease progression) were evaluated. Consequences of each strategy were measured in terms of quality-adjusted life years (QALYs), survival, healthcare costs, and maximum costs to be cost effective.ResultsMost QALYs (6.68) are gained when therapy starts during the pre-ataxic stage and continues during the entire disease course. Incremental costs are lowest (− €14,048) if therapy is stopped when the severe ataxia stage is reached. The maximum costs per year to be cost-effective are €19,630 in the “stop after moderate ataxia stage” strategy at 50% effectiveness.DiscussionOur model indicates that the maximum price for a hypothetical therapy to be cost-effective is considerably lower than currently available RNA-based therapies. Most value for money can be gained by slowing progression in the early and moderate stages of SCA1 and by stopping therapy upon entering the severe ataxia stage. To allow for such a strategy, it is crucial to identify individuals in early stages of disease, preferably just before symptom onset.

Using early health economic modeling to inform medical innovation development: a soft robotic sock in poststroke patients in Singapore.

Based on a real-world collaboration with innovators in applying early health economic modeling, we aimed to offer practical steps that health technology assessment (HTA) researchers and innovators can follow and promote the usage of early HTA among research and development (R&D) communities. The HTA researcher was approached by the innovator to carry out an early HTA ahead of the first clinical trial of the technology, a soft robotic sock for poststroke patients. Early health economic modeling was selected to understand the potential value of the technology and to help uncover the information gap. Threshold analysis was used to identify the target product profiles. Value-of-information analysis was conducted to understand the uncertainties and the need for further research. Based on the expected price and clinical effectiveness by the innovator, the new technology was found to be cost-saving compared to the current practice. Risk reduction in deep vein thrombosis and ankle contracture, the incidence rate of ankle contracture, the compliance rate of the new technology, and utility scores were found to have high impacts on the value-for-money of the new technology. The value of information was low if the new technology can achieve the expected clinical effectiveness. A list of parameters was recommended for data collection in the impending clinical trial. This work, based on a real-world collaboration, has illustrated that early health economic modeling can inform medical innovation development. We provided practical steps in order to achieve more efficient R&D investment in medical innovation moving forward.

Protocol for a MULTI-centre feasibility study to assess the use of 99mTc-sestaMIBI SPECT/CT in the diagnosis of kidney tumours (MULTI-MIBI study)

IntroductionThe incidence of renal tumours is increasing and anatomic imaging cannot reliably distinguish benign tumours from renal cell carcinoma. Up to 30% of renal tumours are benign, with oncocytomas the...

OP07 Dealing With Uncertainty In Early Health Technology Assessment: An Exploration Of Methods For Decision-Making Under Deep Uncertainty

IntroductionIn early stages, the consequences of innovations are often unknown or deeply uncertain. This complicates health economic modelling. The field of decision-making under deep uncertainty (DMDU) uses exploratory modelling (EM) to help decision-makers make sound decisions under conditions of deep uncertainty (i.e., when stakeholders do not know, or cannot agree on, the system model, the probability distributions to place over the inputs to these models, which consequences to consider, and their relative importance). The aim of this research was to evaluate the potential of EM for the early evaluation of health technologies.MethodsEM and early health economic modelling (EHEM) were applied to an early evaluation of minimally invasive surgery (MIS) for acute intracerebral hemorrhage, and were compared to derive differences, merits, and drawbacks of EM.ResultsThe approaches fundamentally differ in the way uncertainty is handled. Where in EHEM the focus is on the value of the technology, while accounting for the uncertainty, EM focuses on the uncertainty. EHEM aims to assess whether the innovative strategy is potentially cost-effective, often using assumptions. EM on the other hand focuses on finding robust strategies (i.e., strategies that give relatively good outcomes over a wide range of plausible futures). This was also reflected in our case study. For example, EHEM provided cost-effectiveness thresholds for MIS effectiveness, assuming fixed MIS costs. EM showed that a strategy with a population in which most patients had severe intracerebral hemorrhage was most robust, regardless of MIS effectiveness, complications, and costs.ConclusionsEM seems most suited in the very early phases of innovation (i.e., when a problem is signaled). Here, it can explore the robustness of many potential strategies under uncertainty. When potential strategies are selected, EHEM seems useful to optimize these strategies. Yet, EM methods are complex and might only be fully effective when a policy window exists that facilitates flexible research and adoption strategies.

Dealing With Uncertainty in Early Health Technology Assessment: An Exploration of Methods for Decision Making Under Deep Uncertainty

ObjectivesIn early stages, the consequences of innovations are often unknown or deeply uncertain, which complicates early health economic modeling (EHEM). The field of decision making under deep uncertainty uses exploratory modeling (EM) in situations when the system model, input probabilities/distributions, and consequences are unknown or debated. Our aim was to evaluate the use of EM for early evaluation of health technologies. MethodsWe applied EM and EHEM to an early evaluation of minimally invasive endoscopy-guided surgery (MIS) for acute intracerebral hemorrhage and compared these models to derive differences, merits, and drawbacks of EM. ResultsEHEM and EM differ fundamentally in how uncertainty is handled. Where in EHEM the focus is on the value of technology, while accounting for the uncertainty, EM focuses on the uncertainty. EM aims to find robust strategies, which give relatively good outcomes over a wide range of plausible futures. This was reflected in our case study. EHEM provided cost-effectiveness thresholds for MIS effectiveness, assuming fixed MIS costs. EM showed that a policy with a population in which most patients had severe intracerebral hemorrhage was most robust, regardless of MIS effectiveness, complications, and costs. ConclusionsEHEM and EM were found to complement each other. EM seems most suited in the very early phases of innovation to explore existing uncertainty and many potential strategies. EHEM seems most useful to optimize promising strategies, yet EM methods are complex and might only add value when stakeholders are willing to consider multiple solutions to a problem and adopt flexible research and adoption strategies.

Early Health Economic Modeling of Novel Therapeutics in Age-Related Hearing Loss.

BackgroundHealth systems face challenges to accelerate access to innovations that add value and avoid those unlikely to do so. This is very timely to the field of age-related sensorineural hearing loss (ARHL), where a significant unmet market need has been identified and sizeable investments made to promote the development of novel hearing therapeutics (NT). This study aims to apply health economic modeling to inform the development of cost-effective NT.MethodsWe developed a decision-analytic model to assess the potential costs and effects of using regenerative NT in patients ≥50 with ARHL. This was compared to the current standard of care including hearing aids and cochlear implants. Input data was collected from systematic literature searches and expert opinion. A UK NHS healthcare perspective was adopted. Three different but related analyses were performed using probabilistic modeling: (1) headroom analysis, (2) scenario analyses, and (3) threshold analyses.ResultsThe headroom analysis shows an incremental net monetary benefit (iNMB) of £20,017[£11,299–£28,737] compared to the standard of care due to quality-adjusted life-years (QALY) gains and cost savings. Higher therapeutic efficacy and access for patients with all degrees of hearing loss yields higher iNMBs. Threshold analyses shows that the ceiling price of the therapeutic increases with more severe degrees of hearing loss.ConclusionNT for ARHL are potentially cost-effective under current willingness-to-pay (WTP) thresholds with considerable room for improvement in the current standard of care pathway. Our model can be used to help decision makers decide which therapeutics represent value for money and are worth commissioning, thereby paving the way for urgently needed NT.

Economic feasibility of a novel tool to assist extubation decision-making: an early health economic modeling.

To estimate the minimum percent change in failed extubation to make a tool designed to reduce extubation failure (Extubation Advisor [EA]) economically viable. We conducted an early return on investment (ROI) analysis using data from intubated intensive care unit (ICU) patients at a large Canadian tertiary care hospital. We obtained input parameters from the hospital database and published literature. We ran generalized linear models to estimate the attributable length of stay, total hospital cost, and time to subsequent extubation attempt following failure. We developed a Markov model to estimate the expected ROI and performed probabilistic sensitivity analyses to assess the robustness of findings. Costs were presented in 2020 Canadian dollars (C$). The model estimated a 1 percent reduction in failed extubation could save the hospital C$289 per intubated patient (95 percent CI: 197, 459). A large center seeing 2,500 intubated ICU patients per year could save C$723,124/year/percent reduction in failed extubation. At the current annual price of C$164,221, the EA tool must reduce extubation failure by at least 0.24 percent (95 percent CI: .14, .41) to make the tool cost-effective at our site. Clinical decision-support tools like the EA may play an important role in reducing healthcare costs by reducing the rate of extubation failure, a costly event in the ICU.

Towards Routine Implementation of Liquid Biopsies in Cancer Management: It Is Always Too Early, until Suddenly It Is Too Late.

Blood-based liquid biopsies are considered a new and promising diagnostic and monitoring tool for cancer. As liquid biopsies only require a blood draw, they are non-invasive, potentially more rapid and assumed to be a less costly alternative to genomic analysis of tissue biopsies. A multi-disciplinary workshop (n = 98 registrations) was organized to discuss routine implementation of liquid biopsies in cancer management. Real-time polls were used to engage with experts’ about the current evidence of clinical utility and the barriers to implementation of liquid biopsies. Clinical, laboratory and health economics presentations were given to illustrate the opportunities and current levels of evidence, followed by three moderated break-out sessions to discuss applications. The workshop concluded that tumor-informed assays using next-generation sequencing (NGS) or PCR-based genotyping assays will most likely provide better clinical utility than tumor-agnostic assays, yet at a higher cost. For routine application, it will be essential to determine clinical utility, to define the minimum quality standards and performance of testing platforms and to ensure their use is integrated into current clinical workflows including how they complement tissue biopsies and imaging. Early health economic models may help identifying the most viable application of liquid biopsies. Alternative funding models for the translation of complex molecular diagnostics, such as liquid biopsies, may also be explored if clinical utility has been demonstrated and when their use is recommended in multi-disciplinary consensus guidelines.

Early health economic modelling for a treatment candidate in hidradenitis suppurativa

Aims Hidradenitis suppurativa (HS) is a chronic skin condition causing inflammatory lesions, pain, scarring, and impaired mobility. Treatment options are limited and often lack success implying the need for additional/improved treatments. This research aimed to estimate the potential economic value of a treatment candidate, explore drivers of cost-effectiveness, and highlight economic evidence requirements for successful future value assessments. Materials and methods An early cost-effectiveness model was developed to assess the cost-effectiveness (cost per quality-adjusted life year (QALY) gained) of a treatment candidate compared against the only authorized biologic, adalimumab, for moderate to severe HS, from a UK National Health Service and Personal Social Service perspective. A targeted literature review of clinical and economic references and previous health technology assessments (HTA) was performed for development and validation of the early economic model used to present sensitivity analyses accompanying the base case cost-effectiveness results. Results and limitations Base case results revealed the candidate not to be cost-effective compared to adalimumab when considering a formal cost-effectiveness threshold of £30,000 per QALY gained. Scenario- and threshold analyses highlighted that reducing dosing or drug price by half improved the cost-effectiveness of the candidate. Cost-effectiveness was highly sensitive to health states’ utility values, treatment discontinuation, and resource utilization, in line with existing HTA evidence. The paucity of economic studies and uncertainties around the candidate presented methodological constraints that were addressed through sensitivity analyses. Conclusions Key costs and health effects drivers were highlighted to contextualize under which circumstances a treatment candidate for moderate to severe HS would reach acceptable cost-effectiveness levels. This early economic evaluation suggested promising economic perspectives for treatment candidates in HS. Exploring novel ways to use clinical endpoints to simulate the patient pathway and clinically meaningful treatment achievements in future research will facilitate the value demonstration of candidates in disease areas with high unmet care need.

The Potential for Early Health Economic Modelling in Health Technology Assessment and Reimbursement Decision-Making Comment on "Problems and Promises of Health Technologies: The Role of Early Health Economic Modeling".

Grutters et al recently investigated the role of early health economic modelling of health technologies by undertaking a secondary analysis of health economic modelling assessments performed by their group. Our commentary offers a broad perspective on the potential utility of early health economic modelling to inform health technology assessment (HTA) and decision-making around reimbursement of new health technologies. Further we provide several examples to compliment Grutters and colleagues’ observations.

Expanding the Role of Early Health Economic Modelling in Evaluation of Health Technologies Comment on "Problems and Promises of Health Technologies: The Role of Early Health Economic Modeling".

In this commentary, we discuss early stage assessments of innovative medical technologies both in terms of methods applied as well as their use in healthcare decision-making. We argue that cost-effectiveness alone may be too reductive if taken as the only decision rule, and it would benefit from being used within a broader evaluation framework. We discuss innovative methods which may contribute to better estimate the potential costs and consequences of a technology in the absence of solid clinical data, as frequently the case in early assessments. Finally, we comment on the potential synergies which may take place should early economic models be used not only by technology developers alone but as a negotiating base during early dialogues with health technology assessment (HTA) bodies.

It's Not the Model, It's the Way You Use It: Exploratory Early Health Economics Amid Complexity Comment on "Problems and Promises of Health Technologies: The Role of Early Health Economic Modelling".

In a review recently published in this journal, Grutters et al outline the scope and impact of their early health economic modelling of healthcare innovations. Their reflections shed light on ways that health economists can shift-away from traditional reimbursement decision-support, towards a broader role of facilitating the exploration of existing care pathways, and the design of options to implement or discontinue healthcare services. This is a crucial role in organisations that face constant pressure to react and adapt with changes to their existing service configurations, but where there may exist significant disagreement and uncertainty on the extent to which change is warranted. Such dynamics are known to create complex implementation environments, where changes risk being poorly implemented or fail to be sustained. In this commentary, we extend the discussion by Grutters et al on early health economic modelling, to the evaluation of complex interventions and systems. We highlight how early health economic modelling can contribute to a participatory approach for ongoing learning and development within healthcare organisations.

Early Health Economic Modelling - Optimizing Development for Medical Device Developers? Comment on "Problems and Promises of Health Technologies: The Role of Early Health Economic Modeling".

This commentary considers the positive and negative consequences of early economic modelling and explores potential future directions. Early economic modelling offers device manufacturers an opportunity to assess the potential value of an innovation at an early stage of development. Early modelling can direct resources into potentially viable technologies and reduce investment in technologies with limited prospect of value. However, it is unclear whether early modelling is sufficiently specific to identify innovations with low value. It may be that early modelling is more useful for directing data gathering to reduce decision uncertainty. Early modelling is of primary benefit to the manufacturer and may have both positive and negative consequences for reimbursement processes that should be considered.

Characterizing the Validity and Real-World Utility of Health Technology Assessments in Healthcare: Future Directions Comment on "Problems and Promises of Health Technologies: The Role of Early Health Economic Modelling".

With their article, Grutters et al raise an important question: What do successful health technology assessments (HTAs) look like, and what is their real-world utility in decision-making? While many HTAs are published in peer-reviewed journals, many are considered proprietary and their attributes remain confidential, limiting researchers’ ability to answer these questions. Models for economic evaluations like cost-effectiveness analyses (CEAs) synthesize a wide range of evidence, are often statistically and mathematically sophisticated, and require untestable assumptions. As such, there is nearly universal agreement among researchers that enhancing transparency is an important issue in health economic modeling. However, the definition of transparency and guidelines for its implementation vary. Model registration combined with a linked database of model-based economic evaluations has been proposed as a solution, whereby registered models and their accompanying technical and nontechnical documentation are sourced into a single publicly-available repository, ideally in a standardized format to ensure consistent and complete representation of features, code, data sources, results, validation exercises, and policy recommendations. When such a repository is ultimately created, modelers will not have to reinvent the wheel for every new drug launched or new treatment pathway. These more open and transparent approaches will have substantial implications for model accuracy, reliability, and validity, improving trust and acceptance by healthcare decision-makers.

Transforming Disciplinary Traditions Comment on "Problems and Promises of Health Technologies: The Role of Early Health Economic Modeling".

Grutters et al show that economic assessments can inform the development of new health technologies at an early stage. This is an important contribution to health services and policy research, which implies a "shift away" from the more traditional forms of academic health economic modeling. Because transforming established disciplinary traditions is both valuable and demanding, we invite scholars to further the discussion on how the value of health innovations should be appraised in view of today’s societal challenges.

Modeling in Early Stages of Technology Development: Is an Iterative Approach Needed? Comment on "Problems and Promises of Health Technologies: The Role of Early Health Economic Modeling".

A recent paper by Grutters et al makes the case for early health economic modeling in the development of health technologies. A number of examples of the value of early modeling are given, with analyses being performed at different stages in the development of several non-drug health technologies. This commentary acknowledges the contribution of the paper by Grutters et al and argues for an iterative and integrated approach to early modeling, assessing the cost-effectiveness of the technology, the value of future research and the interaction with the manufacturer’s pricing and revenue expectations.

How Useful Are Early Economic Models? Comment on "Problems and Promises of Health Technologies: The Role of Early Health Economic Modelling".

Early economic modelling has long been recommended to aid research and development (R&D) decisions in medical innovation, although they are less frequently published and critically appraised. A review of 30 innovations by Grutters et al provides an opportunity to evaluate how early models are used in practice. The evidence of early models can be used to inform two types of decision: to continue development ("stop or go") or to alter future R&D activities. I argue that early models have limited use in stop or go decisions, as less resource and data undermine the reliability of the models’ indicative estimates of cost-effectiveness. Whilst they are far more useful for informing future R&D directions, the best techniques available from statistical decision science, such as value of information analysis, are not regularly used. It is highly recommended that early models adopt these methods to best deal with uncertainty, quantify the potential value of further research, identify areas of study with the greatest potential benefit and generate recommendations on study design and sample size.

Clinical biomarker innovation: when is it worthwhile?

Background Choosing which biomarker tests to select for further research and development is not only a matter of diagnostic accuracy, but also of the clinical and monetary benefits downstream. Early health economic modeling provides tools to assess the potential effects of biomarker innovation and support decision-making. Methods We applied early health economic modeling to the case of diagnosing primary aldosteronism in patients with resistant hypertension. We simulated a cohort of patients using a Markov cohort state-transition model. Using the headroom method, we compared the currently used aldosterone-to-renin ratio to a hypothetical new test with perfect diagnostic properties to determine the headroom based on quality-adjusted life-years (QALYs) and costs, followed by threshold analyses to determine the minimal diagnostic accuracy for a cost-effective product. Results Our model indicated that a perfect diagnostic test would yield 0.027 QALYs and increase costs by €43 per patient. At a cost-effectiveness threshold of €20,000 per QALY, the maximum price for this perfect test to be cost-effective is €498 (95% confidence interval [CI]: €275-€808). The value of the perfect test was most strongly influenced by the sensitivity of the current biomarker test. Threshold analysis showed the novel test needs a sensitivity of at least 0.9 and a specificity of at least 0.7 to be cost-effective. Conclusions Our model-based approach evaluated the added value of a clinical biomarker innovation, prior to extensive investment in development, clinical studies and implementation. We conclude that early health economic modeling can be a valuable tool when prioritizing biomarker innovations in the laboratory.