Modeling the Early Identification and Intervention of Alzheimer’s Disease

Abstract

Advances in neuroimaging and biomarkers now provide the ability to detect evidence for the pathophysiological process of Alzheimer’s disease (AD) before clinically detectable dementia. Because of these findings, AD research has begun to focus on the preclinical or prodromal stages of the disease. For example, many clinical trials and laboratory-based studied have examined the clinical benefit of earlier AD intervention, such as pre-symptomatic stages of AD, based on the belief that it is more likely to achieve disease modification. The economic evaluation of potential interventions on AD, which mainly extends to include the earlier disease stages by using biomarker testing to predict the risk of disease progression, needs to be updated. Accordingly, the overall objective of this thesis is to quantify the value of using cerebrospinal fluid (CSF) biomarker testing for early-targeted treatment on patients with mild cognitive impairment who are at risk of developing AD. Firstly, I examined whether CSF biomarker testing can categorize MCI patients into different risk groups of developing AD, and thus allowing for targeted early treatment on MCI patients. Secondly, I conducted a cost-effectiveness analysis to evaluate the different treatment strategies with or without testing information involved by developing a decision model to synthesize all relevant evidence and project the expected value of outcomes of interest for each proposed alternative. Finally, I further address key challenges based on the current evidence by estimating the societal value of reducing uncertainty surrounding the decision model through further research. Economic evidence about the relative costs and outcomes of health and social care can assist decision makers in determining the best use of scarce healthcare resources.