Stephanie Evans a , ∗ , Kevin McRae-McKee a , Christoforos Hadjichrysanthou a , Mei Mei Wong a , David Ames b , c , Oscar Lopez d , Frank de Wolf a , e , Roy M. Anderson a , Australian Imaging Biomarkers and Lifestyle flagship study of ageing 2 , Predictors of Cognitive Decline Among Normal Individuals (BIOCARD) study 3 , Add Neuro Med Consortium
03 October 2019
There exist a large number of cohort studies that have been used to identify genetic and biological risk factors for developing Alzheimer's disease (AD). However, there is a disagreement between studies as to how strongly these risk factors affect the rate of progression through diagnostic groups toward AD. We have calculated the probability of transitioning through diagnostic groups in six studies and considered how uncertainty around the strength of the effect of these risk factors affects estimates of the distribution of individuals in each diagnostic group in an AD clinical trial simulator. In this work, we identify the optimal choice of widely collected variables for comparing data sets and calculating probabilities of progression toward AD. We use the estimated transition probabilities to inform stochastic simulations of AD progression that are based on a Markov model and compare predicted incidence rates to those in a community-based study, the Cardiovascular Health Study.