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IPECAD Modeling Workshop 2023 Cross-Comparison Challenge on Cost-Effectiveness Models in Alzheimer's Disease
R. Handels, WL. Herring, F. Kamgar, S. Aye, A. Tate, C. Green, A. Gustavsson, A. Wimo, B. Winblad, A. Sköldunger, LL. Raket, CB. Stellick, E. Spackman, J. Hlávka, Y. Wei, J. Mar, M. Soto-Gordoa, I. de Kok, C. Brück, R. Anderson, P....
Language English Country United States
Document type Journal Article, Comparative Study
- MeSH
- Alzheimer Disease * economics drug therapy MeSH
- Cost-Benefit Analysis * MeSH
- Models, Economic MeSH
- Cognitive Dysfunction * economics MeSH
- Quality-Adjusted Life Years MeSH
- Humans MeSH
- Decision Support Techniques MeSH
- Disease Progression MeSH
- Treatment Outcome MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Comparative Study MeSH
OBJECTIVES: Decision-analytic models assessing the value of emerging Alzheimer's disease (AD) treatments are challenged by limited evidence on short-term trial outcomes and uncertainty in extrapolating long-term patient-relevant outcomes. To improve understanding and foster transparency and credibility in modeling methods, we cross-compared AD decision models in a hypothetical context of disease-modifying treatment for mild cognitive impairment (MCI) due to AD. METHODS: A benchmark scenario (US setting) was used with target population MCI due to AD and a set of synthetically generated hypothetical trial efficacy estimates. Treatment costs were excluded. Model predictions (10-year horizon) were assessed and discussed during a 2-day workshop. RESULTS: Nine modeling groups provided model predictions. Implementation of treatment effectiveness varied across models based on trial efficacy outcome selection (clinical dementia rating - sum of boxes, clinical dementia rating - global, mini-mental state examination, functional activities questionnaire) and analysis method (observed severity transitions, change from baseline, progression hazard ratio, or calibration to these). Predicted mean time in MCI ranged from 2.6 to 5.2 years for control strategy and from 0.1 to 1.0 years for difference between intervention and control strategies. Predicted quality-adjusted life-year gains ranged from 0.0 to 0.6 and incremental costs (excluding treatment costs) from -US$66 897 to US$11 896. CONCLUSIONS: Trial data can be implemented in different ways across health-economic models leading to large variation in model predictions. We recommend (1) addressing the choice of outcome measure and treatment effectiveness assumptions in sensitivity analysis, (2) a standardized reporting table for model predictions, and (3) exploring the use of registries for future AD treatments measuring long-term disease progression to reduce uncertainty of extrapolating short-term trial results by health-economic models.
Biogen Idec Ltd Maidenhead England UK
Biogen International GmbH Baar Switzerland
Biogipuzkoa Health Research Institute Donostia San Sebastián Spain
Biosistemak Institute for Health Service Research Barakaldo Spain
Care Policy and Evaluation Centre London School of Economics London England UK
Clinical Memory Research Unit Department of Clinical Sciences Lund University Lund Sweden
Department of Public Health Erasmus MC University Medical Center Rotterdam Rotterdam The Netherlands
Health Economics Policy and Innovation Institute Masaryk University Brno Czech Republic
Health Economics RTI Health Solutions Research Triangle Park NC USA
Quantify Research Stockholm Sweden
Theme Inflammation and Aging Karolinska University Hospital Huddinge Sweden
References provided by Crossref.org
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- $a Handels, Ron $u Alzheimer Centre Limburg, Faculty of Health Medicine and Life Sciences, School for Mental Health and Neuroscience, Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, The Netherlands; Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden. Electronic address: ron.handels@maastrichtuniversity.nl
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