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IPECAD MODELING WORKSHOP 2023 CROSS COMPARISON CHALLENGE ON COST-EFFECTIVENESS MODELS IN ALZHEIMER'S DISEASE AND RELATED DEMENTIAS
Autoři | |
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Rok publikování | 2023 |
Druh | Článek v odborném periodiku |
Časopis / Zdroj | VALUE IN HEALTH |
Fakulta / Pracoviště MU | |
Citace | |
Doi | http://dx.doi.org/10.1016/j.jval.2024.09.006 |
Klíčová slova | Alzheimer’s disease; decision-analytic modeling; health-economic evaluation; cross-validation |
Popis | 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 (CDR-SB, CDR-global, MMSE, FAQ) and analysis method (observed severity transitions, change from baseline, progression hazard ratio, or calibration to these). Predicted mean time in MCI ranged from 2.6-5.2 years for control strategy, and from 0.1-1.0 years for difference between intervention and control strategies. Predicted quality-adjusted life-year gains ranged from 0.0-0.6 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. |