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.
