Development and Use of Disease-Specific (Reference) Models for Economic Evaluations of Health Technologies: An Overview of Key Issues and Potential Solutions

Frederix, G.W.; Afzali, Hossein Haji Ali; Dasbach, Erik J.; Ward, Robyn L.

doi:10.1007/s40273-015-0274-y

Cited by 9 publications

(7 citation statements)

References 27 publications

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“…In 2011, we proposed the development and use of reference models to inform reimbursement decisions [11]. This has been reiterated by Frederix et al in 2014 [5] and 2015 [12], and, more recently (2019), by Mauskopf [13] and Sampson et al [14]. Using the best available evidence and data sources, reference models represent a specific disease (e.g.…”

Section: Key Limitations Underlying the Need For (Disease-specific) Rmentioning

confidence: 99%

Improving Decision-Making Processes in Health: Is It Time for (Disease-Specific) Reference Models?

Afzali

Bojke

Karnon

2019

Appl Health Econ Health Policy

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Decision-analytic models are often required to evaluate the value of new health technologies such as medical devices and pharmaceuticals. Within health technology assessment (HTA) submissions, the role of modelling is principally to assess whether a proposed new technology is cost effective by predicting its long-term costs and benefits compared with relevant comparators such as usual care. In countries such as Australia and the UK, such evidence is commonly used as one of the key inputs into the decision-making process to inform resource allocation decisions. Given the impact of model-based evaluations on funding decisions, it is critical to develop and consistently use credible models. However, important concerns about decision-analytic modelling still remain [1]. In this editorial, we focus on one important concern within the decision-making process: the impact of alternative model structural choices on the comparability and accuracy of economic evaluations, and how (disease-specific) reference models can potentially address this.

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Section: Key Limitations Underlying the Need For (Disease-specific) Rmentioning

confidence: 99%

Improving Decision-Making Processes in Health: Is It Time for (Disease-Specific) Reference Models?

Afzali

Bojke

Karnon

2019

Appl Health Econ Health Policy

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“…Several authors have addressed ideas similar to that of MUDMs with different names, for instance ‘whole disease models’ by Tappenden et al [ 2 ], ‘reference models’ by Afzali et al and Frederix et al [ 5 – 7 ], ‘policy model’ by Weinstein et al [ 8 ] and the ‘treatment pathways models’ in Lord’s MAPGuide project [ 9 ]. Finally, ‘generic models’ were coined by Snyder et al [ 10 ].…”

Section: Resultsmentioning

confidence: 99%

A Blueprint for Multi-use Disease Modeling in Health Economics: Results from Two Expert-Panel Consultations

Wang,

Pouwels,

Ramaekers

et al. 2024

PharmacoEconomics

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Background The current use of health economic decision models in HTA is mostly confined to single use cases, which may be inefficient and result in little consistency over different treatment comparisons, and consequently inconsistent health policy decisions, for the same disorder. Multi-use disease models (MUDMs) (other terms: generic models, whole disease models, disease models) may offer a solution. However, much is uncertain about their definition and application. The current research aimed to develop a blueprint for the application of MUDMs. Methods We elicited expert opinion using a two-round modified Delphi process. The panel consisted of experts and stakeholders in health economic modelling from various professional backgrounds. The first questionnaire concerned definition, terminology, potential applications, issues and recommendations for MUDMs and was based on an exploratory scoping review. In the second round, the panel members were asked to reconsider their input, based on feedback regarding first-round results, and to score issues and recommendations for priority. Finally, adding input from external advisors and policy makers in a structured way, an overview of issues and challenges was developed during two team consensus meetings. Results In total, 54 respondents contributed to the panel results. The term ‘multi-use disease models’ was proposed and agreed upon, and a definition was provided. The panel prioritized 10 potential applications (with comparing alternative policies and supporting resource allocation decisions as the top 2), while 20 issues (with model transparency and stakeholders’ roles as the top 2) were identified as challenges. Opinions on potential features concerning operationalization of multi-use models were given, with 11 of these subsequently receiving high priority scores ( regular updates and revalidation after updates were the top 2). Conclusions MUDMs would improve on current decision support regarding cost-effectiveness information. Given feasibility challenges, this would be most relevant for diseases with multiple treatments, large burden of disease and requiring more complex models. The current overview offers policy makers a starting point to organize the development, use, and maintenance of MUDMs and to support choices concerning which diseases and policy decisions they will be helpful for. Supplementary Information The online version contains supplementary material available at 10.1007/s40273-024-01376-w.

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“…Fourth, efforts should be made to develop a reference model because heterogeneity in modelling studies for assessing preventive strategies for BC/OC was not limited to utilities but also observed for other aspects (e.g., model structure, tools used for collecting clinical and epidemiological data, methods of validation) [ 6 ]. As demonstrated for other diseases, a reference model ensures new models will be populated based on the systematic identification of the best available data [ 58 , 59 ]. This would increase the likelihood of models to be consistent and comparable across different countries.…”

Section: Discussionmentioning

confidence: 99%

Data on Utility in Cost–Utility Analyses of Genetic Screen-and-Treat Strategies for Breast and Ovarian Cancer

Galendi

Vennedey

Kentenich

et al. 2021

Cancers

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Genetic screen-and-treat strategies for the risk-reduction of breast cancer (BC) and ovarian cancer (OC) are often evaluated by cost–utility analyses (CUAs). This analysis compares data on health preferences (i.e., utility values) in CUAs of targeted genetic testing for BC and OC. Based on utilities applied in fourteen CUAs, data on utility including related assumptions were extracted for the health states: (i) genetic test, (ii) risk-reducing surgeries, (iii) BC/OC and (iv) post cancer. In addition, information about the sources of utility and the impact on the cost-effectiveness was extracted. Utility for CUAs relied on heterogeneous data and assumptions for all health states. The utility values ranged from 0.68 to 0.97 for risk-reducing surgeries, 0.6 to 0.85 for BC and 0.5 to 0.82 for OC. In two out of nine studies, considering the impact of the test result strongly affected the cost–effectiveness ratio. While in general utilities seem not to affect the cost–utility ratio, in future modeling studies the impact of a positive/negative test on utility should be considered mandatory. Women’s health preferences, which may have changed as a result of improved oncologic care and genetic counselling, should be re-evaluated.

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Development and Use of Disease-Specific (Reference) Models for Economic Evaluations of Health Technologies: An Overview of Key Issues and Potential Solutions

Cited by 9 publications

References 27 publications

Improving Decision-Making Processes in Health: Is It Time for (Disease-Specific) Reference Models?

Improving Decision-Making Processes in Health: Is It Time for (Disease-Specific) Reference Models?

A Blueprint for Multi-use Disease Modeling in Health Economics: Results from Two Expert-Panel Consultations

Data on Utility in Cost–Utility Analyses of Genetic Screen-and-Treat Strategies for Breast and Ovarian Cancer

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