Systematic Review of Model-Based Economic Evaluations of Treatments for Alzheimer’s Disease

Numerous economic evaluations using decision-analytic models have assessed the cost effectiveness of treatments for Alzheimer’s disease (AD) in the last two decades. It is important to understand the methods used in the existing models of AD and how they could impact results, as they could inform new model-based economic evaluations of treatments for AD.

The aim of this systematic review was to provide a detailed description on the relevant aspects and components of existing decision-analytic models of AD, identifying areas for improvement and future development, and to conduct a quality assessment of the included studies.

We performed a systematic and comprehensive review of cost-effectiveness studies of pharmacological treatments for AD published in the last decade (January 2005 to February 2015) that used decision-analytic models, also including studies considering patients with mild cognitive impairment (MCI). The background information of the included studies and specific information on the decision-analytic models, including their approach and components, assumptions, data sources, analyses, and results, were obtained from each study. A description of how the modeling approaches and assumptions differ across studies, identifying areas for improvement and future development, is provided. At the end, we present our own view of the potential future directions of decision-analytic models of AD and the challenges they might face.

The included studies present a variety of different approaches, assumptions, and scope of decision-analytic models used in the economic evaluation of pharmacological treatments of AD. The major areas for improvement in future models of AD are to include domains of cognition, function, and behavior, rather than cognition alone; include a detailed description of how data used to model the natural course of disease progression were derived; state and justify the economic model selected and structural assumptions and limitations; provide a detailed (rather than high-level) description of the cost components included in the model; and report on the face-, internal-, and cross-validity of the model to strengthen the credibility and confidence in model results. The quality scores of most studies were rated as fair to good (average 87.5, range 69.5–100, in a scale of 0–100).

Despite the advancements in decision-analytic models of AD, there remain several areas of improvement that are necessary to more appropriately and realistically capture the broad nature of AD and the potential benefits of treatments in future models of AD.