Introduction: Alzheimer's disease (AD) is a complex neurodegenerative disease, affecting millions of people worldwide and imposing heavy economic burdens to societies. Currently, only symptomatic treatments are available for patients, but there is ongoing research on potential therapies that can modify the course of disease. The main objective of this work is to identify and explore the challenges surrounding decision modeling for economic evaluation of interventions for AD.
Areas covered: This article discusses the challenges in modeling the natural history of disease, particularly regarding the selection of disease progression and outcome measures, the inclusion of biomarker status in models, and the approach to model mortality. Challenges stemming from the use of long-term assumptions regarding treatment effects and the need for real-world evidence to fill data gaps are discussed. Lastly, the overwhelming economic impact of disease and the challenges in estimating these costs for modeling are addressed.
Expert opinion: Value assessment frameworks need to be reconsidered in order to demonstrate the full benefit of new disease-modifying therapies spanning beyond the scope of health systems. Data collection efforts that expand the evidence base, upon which economic models are based, will reduce the uncertainties surrounding the long-term outcomes of interventions in AD.
Keywords: Alzheimer’s disease; cost-effectiveness; dementia; economic evaluation; health policy; modeling; value assessment.