IPECAD Modeling Workshop 2023 Cross-Comparison Challenge on Cost-Effectiveness Models in Alzheimer's Disease.

Autor: Handels R; 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., Herring WL; Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden; Health Economics, RTI Health Solutions, Research Triangle Park, NC, USA., Kamgar F; Health Economics, RTI Health Solutions, Research Triangle Park, NC, USA., Aye S; Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden., Tate A; Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden., Green C; Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden; Biogen Idec Ltd, Maidenhead, England, UK., Gustavsson A; Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden; Quantify Research, Stockholm, Sweden., Wimo A; Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden., Winblad B; Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden; Theme Inflammation and Aging, Karolinska University Hospital, Huddinge, Sweden., Sköldunger A; Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden., Raket LL; Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden., Stellick CB; Community Health Sciences & O'Brien Institute of Public Health, University of Calgary, Calgary, Alberta, Canada., Spackman E; Community Health Sciences & O'Brien Institute of Public Health, University of Calgary, Calgary, Alberta, Canada., Hlávka J; Health Economics, Policy and Innovation Institute, Masaryk University, Brno, Czech Republic; USC Price School of Public Policy and Schaeffer Center for Health Policy and Economics, Los Angeles, CA, USA., Wei Y; USC Price School of Public Policy and Schaeffer Center for Health Policy and Economics, Los Angeles, CA, USA., Mar J; Basque Health Service (Osakidetza), Debagoiena Integrated Healthcare Organisation, Research Unit, Arrasate-Mondragón, Spain; Biogipuzkoa Health Research Institute, Donostia-San Sebastián, Spain; Biosistemak Institute for Health Service Research, Barakaldo, Spain., Soto-Gordoa M; Faculty of Engineering, Electronics and Computing Department, Mondragon Unibertsitatea, Mondragon, Gipuzkoa, Spain., de Kok I; Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands., Brück C; Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands., Anderson R; Care Policy and Evaluation Centre, London School of Economics, London, England, UK., Pemberton-Ross P; Biogen International GmbH, Baar, Switzerland., Urbich M; Biogen International GmbH, Baar, Switzerland., Jönsson L; Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden.
Jazyk: angličtina
Zdroj: Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research [Value Health] 2024 Oct 08. Date of Electronic Publication: 2024 Oct 08.
DOI: 10.1016/j.jval.2024.09.006
Abstrakt: 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.
Competing Interests: Author Disclosures Author disclosure forms can be accessed below in the Supplemental Material section.
(Copyright © 2024. Published by Elsevier Inc.)
Databáze: MEDLINE
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