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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., (Copyright © 2024. Published by Elsevier Inc.)

Objectives: Reimbursement decisions for new Alzheimer's disease (AD) treatments are informed by economic evaluations. An open-source model with intuitive structure for model cross-validation can support the transparency and credibility of such evaluations. We describe the new International Pharmaco-Economic Collaboration on Alzheimer's Disease (IPECAD) open-source model framework (version 2) for the health-economic evaluation of early AD treatment and use it for cross-validation and addressing uncertainty., Methods: A cohort state-transition model using a categorized composite domain (cognition and function) was developed by replicating an existing reference model and testing it for internal validity. Then, features of existing Institute for Clinical and Economic Review (ICER) and Alzheimer's Disease Archimedes Condition-Event Simulator (AD-ACE) models assessing lecanemab treatment were implemented for model cross-validation. Additional uncertainty scenarios were performed on choice of efficacy outcome from trial, natural disease progression, treatment effect waning and stopping rules, and other methodological choices. The model is available open-source as R code, spreadsheet, and web-based version via https://github.com/ronhandels/IPECAD., Results: In the IPECAD model incremental life-years, quality-adjusted life-years (QALY) gains and cost savings were 21% to 31% smaller compared with the ICER model and 36% to 56% smaller compared with the AD-ACE model. IPECAD model results were particularly sensitive to assumptions on treatment effect waning and stopping rules and choice of efficacy outcome from trial., Conclusions: We demonstrated the ability of a new IPECAD open-source model framework for researchers and decision makers to cross-validate other (Health Technology Assessment submission) models and perform additional uncertainty analyses, setting an example for open science in AD decision modeling and supporting important reimbursement decisions., Competing Interests: Author Disclosures Author disclosure forms can be accessed below in the Supplemental Material section., (Copyright © 2024. Published by Elsevier Inc.)

Objectives: Model-based cost-effectiveness analyses on maternal vaccine (MV) and monoclonal antibody (mAb) interventions against respiratory syncytial virus (RSV) use context-specific data and produce varied results. Through model comparison, we aim to characterize RSV cost-effectiveness models and examine drivers for their outputs., Methods: We compared 3 static and 2 dynamic models using a common input parameter set for a hypothetical birth cohort of 100 000 infants. Year-round and seasonal programs were evaluated for MV and mAb interventions, using available evidence during the study period (eg, phase III MV and phase IIb mAb efficacy)., Results: Three static models estimated comparable medically attended (MA) cases averted versus no intervention (MV, 1019-1073; mAb, 5075-5487), with the year-round MV directly saving ∼€1 million medical and €0.3 million nonmedical costs, while gaining 4 to 5 discounted quality-adjusted life years (QALYs) annually in <1-year-olds, and mAb resulting in €4 million medical and €1.5 million nonmedical cost savings, and 21 to 25 discounted QALYs gained. In contrast, both dynamic models estimated fewer MA cases averted (MV, 402-752; mAb, 3362-4622); one showed an age shift of RSV cases, whereas the other one reported many non-MA symptomatic cases averted, especially by MV (2014). These differences can be explained by model types, assumptions on non-MA burden, and interventions' effectiveness over time., Conclusions: Our static and dynamic models produced overall similar hospitalization and death estimates, but also important differences, especially in non-MA cases averted. Despite the small QALY decrement per non-MA case, their larger number makes them influential for the costs per QALY gained of RSV interventions., (Copyright © 2023. Published by Elsevier Inc.)