Handels R, Herring W, Kamgar F, Gustavsson A, Skoldunger A, Wimo A, Tate A, Winblad B, Stellick CB, Bruck C, Green C, de Kok I, Hlavka J, Mar J, Urbich M, Soto-Gordoa M, Pemberton-Ross P, Aye S, Jonsson L, workshop 2023 Participants I. IPECAD modeling workshop 2023 cross comparison challenge on cost-effectiveness models in Alzheimer's disease and related dementias. Poster presented at the ISPOR Europe 2023; November 15, 2023. Copenhagen, Denmark. [abstract] Value Health. 2023 Dec; 26(12 Supplement):S419. doi: 10.1016/j.jval.2023.09.2194

OBJECTIVES: Decision-analytic models used to assess 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, transparency and credibility of their results we cross-compared AD decision-analytic models for a hypothetical disease-modifying treatment starting in mild cognitive impairment (MCI) due to AD.

METHODS: Eight independent modeling groups implemented a benchmark scenario with target population MCI due to AD, U.S. setting (mortality, costs and utilities) and a set of plausible hypothetical trial efficacy estimates (synthetic trial data and summary tables for intervention and control arm) with no treatment costs. Model outcomes were summarized and discussed during a 2-day workshop.

RESULTS: Treatment implementation varied based on selection of trial effcacy outcome (e.g., CDR-SB, CDR-global, MMSE, FAQ) and methodology (e.g., observed transitions, calibration to change from baseline, hazard ratio). Mean 10-year time in MCI ranged from 2.6-5.2 years for the control strategy, and the difference between intervention and control strategy ranged from 0.1-1.0 years. This was likely driven by selected trial outcome, which ranged from 7%-35% in terms of relative treatment effect. Quality-adjusted life-year gains ranged from 0.0-0.6 and incremental costs from -$66,897 to $11,896.

CONCLUSIONS: The variation in outcomes for the current cross-comparison, which focused on treatment effect implementation, was similar to previous AD model comparisons that emphasized different model types and natural history data sources. Based on our cross-comparison outcomes, we recommend that decision-makers consider a set of plausible sensitivity analyses based on methodology for implementing treatment effect. For modelers we suggest standardized reporting of model outcomes for which we presented a set of tables. For future AD treatment evaluation we recommend a registry for measuring disease progression after discontinuation given the importance of these required assumptions as a large driver of uncertainty in the results.

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