Mathematical prediction of neurodegenerative disease' progression
2017
University of Western Australia, Crawley, Australia
One of the limitations of epidemiological studies is the lack of long-term data of longitudinal studies. To overcome this problem, this study presents a mathematical model to infer the underlying long-term trajectories of short-term sparse follow-up data from Alzheimer's disease studies. Through a step-wise method, the researchers are able to build a model that can reliably predict the disease progression curve, allowing them to build the sigmoidal trajectories of the disease. As a demonstration, throughout the study, they were able to quantify the long-term progression of the pathogenesis of amyloid-beta burden in the neocortex with the data from the Alzheimer's Disease Neuroimaging Initiative. In summary, this predictor model will be helpful to overcome the limitations of epidemiological studies in which participants' data collection has been abruptly interrupted and make it possible to quantify and understand full disease progression predicting long-term epidemiological data of neurodegenerative diseases.
Constructing longitudinal disease progression curves using sparse, short-term individual data with an application to Alzheimer's disease
C A Budgeon
Added on: 08-04-2021
[1] https://onlinelibrary.wiley.com/doi/10.1002/sim.7300[2] https://data.jrc.ec.europa.eu/dataset/a8fd26ef-b113-47ab-92ba-fd2be449c7eb
