Incorporating clinical trial outcomes in health economic evaluations of disease‐modifying therapies for AD

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AbstractBackgroundEvidence on cost‐effectiveness of novel therapies for Alzheimer’s disease (AD) is of increasing importance for patient access. Challenges involved in implementing treatment effects in health economic (HE) models is an important source of uncertainty around the value of new AD therapies. HE models are developed from observational studies that often include different patient populations and different endpoints and scales from clinical trials.MethodsThe International Pharmacoeconomic Collaboration on Alzheimer’s Disease (IPECAD) conducted a series of workshops with research groups developing HE models in AD, to systematically characterize methodological approaches to modelling and benchmark results from these models in standardized scenarios. We reviewed published models and contrasted the characteristics of these models with study design specifications of recent late‐stage clinical trials of AD DMT to highlight potential issues with incorporating efficacy data from clinical trials in existing HE models. Identified issues included: need for mapping between different scales used to assess cognition, function and other efficacy domains, incorporating effects on multiple domains, differences in trial populations and expected patient populations treated in routine care, and statistical methods for generating efficacy estimates for use in HE models.ResultsThree options were identified for translating trial efficacy estimates into HE models: 1) use actual trial data and model disease course post trial, 2) assume treatment reduces transition probabilities to more severe states, and 3) implement treatment effect as a proportional reduction of progression on the time scale.ConclusionsThe design of future clinical trials of DMT need to take into account the need to generate data to populate HE models. Pre‐specifying analyses of trial data for HE modeling purposes can reduce uncertainty and improve confidence in results.