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 outcomes (10-year horizon) were assessed and discussed during a 2-day workshop.
RESULTS: Estimates were provided by 9 modeling groups. Implementation of treatment effectiveness varied across models based on trial efficacy outcome selection (CDR-SB, CDR-global, MMSE, FAQ) and analysis method (e.g., observed severity transitions, change from baseline, progression hazard ratio, or calibration to these). 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. 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 outcomes. We recommend 1) addressing the choice of outcome measure and treatment effectiveness assumptions in sensitivity analysis, 2) standardized reporting table for model outcomes, and 3) explore the using registries for future AD treatments measuring long-term disease progression to reduce the uncertainty of extrapolating short-term trial results by health economic models.
