Estimating the Value of New Alzheimer’s Disease Therapies and Early Disease Screening
Author(s)
Moderator: Scott Johnson, PhD, Medicus Economics, LLC, Milton, MA, USA
Panelists: Ron Handels, PhD, Alzheimer Centrum Limburg, Maastricht University, maastricht, Netherlands; Jakub Hlavka, PhD, Schaeffer Center for Health Policy and Economics, Sol Price School of Public Policy, University of Southern California, Los Angeles, CA, USA; Inge M.C.M. de Kok, PhD, Department of Public Health, Erasmus University Medical Center, Rotterdam, Netherlands
Presentation Documents
ISSUE: Using simulation models to estimate the value of new disease-modifying therapies in Alzheimer’s disease and early screening, and the value of their comparison.
OVERVIEW: In June 2021, the FDA conditionally approved a new drug treatment for Alzheimer’s disease for persons in a pre-dementia state of the disease. Establishing its health-economic value comes with several challenges, among which is establishing the natural course by analyzing clinical and registry data and implementing assumptions on long-term treatment effectiveness. Various models have been developed, but comparisons between them have been challenging, particularly as some models are more parsimonious than others and differ in their primary objectives.
This panel will address key challenges associated with simulation modeling of Alzheimer’s disease-modifying treatments (DMTs), and provide insights about the importance of model inputs, model design choices, and the interpretation of results. This panel will begin with an introduction by the moderator (5 minutes), followed by brief presentations by each of the three panelists (3-5 minutes). The remaining time will be for open discussion led by the moderator. The panel will highlight key lessons learned from model cross-comparison organized by the International Pharmacoeconomic Collaboration on Alzheimer’s Disease (www.ipecad.org). Panelists will share their insights and takeaways from Alzheimer’s modeling in Europe and the United StatesConference/Value in Health Info
Code
106
Topic
Study Approaches