  Alzheimer's disease (AD) is the most common form of dementia and is
phenotypically heterogeneous. APOE is a triallelic gene which correlates with
phenotypic heterogeneity in AD. In this work, we determined the effect of APOE
alleles on the disease progression timeline of AD using a discriminative
event-based model (DEBM). Since DEBM is a data-driven model, stratification
into smaller disease subgroups would lead to more inaccurate models as compared
to fitting the model on the entire dataset. Hence our secondary aim is to
propose and evaluate novel approaches in which we split the different steps of
DEBM into group-aspecific and group-specific parts, where the entire dataset is
used to train the group-aspecific parts and only the data from a specific group
is used to train the group-specific parts of the DEBM. We performed simulation
experiments to benchmark the accuracy of the proposed approaches and to select
the optimal approach. Subsequently, the chosen approach was applied to the
baseline data of 417 cognitively normal, 235 mild cognitively impaired who
convert to AD within 3 years, and 342 AD patients from the Alzheimer's Disease
Neuroimaging Initiative (ADNI) dataset to gain new insights into the effect of
APOE carriership on the disease progression timeline of AD. The presented
models could aid understanding of the disease, and in selecting homogeneous
group of presymptomatic subjects at-risk of developing symptoms for clinical
trials.
