Prediction of Conversion to Alzheimer's Disease with Longitudinal Measures and Time-To-Event Data.

ABSTRACT:

Background

Identifying predictors of conversion to Alzheimer's disease (AD) is critically important for AD prevention and targeted treatment.

Objective

To compare various clinical and biomarker trajectories for tracking progression and predicting conversion from amnestic mild cognitive impairment to probable AD.

Methods

Participants were from the ADNI-1 study. We assessed the ability of 33 longitudinal biomarkers to predict time to AD conversion, accounting for demographic and genetic factors. We used joint modelling of longitudinal and survival data to examine the association between changes of measures and disease progression. We also employed time-dependent receiver operating characteristic method to assess the discriminating capability of the measures.

Results<

SUBMITTER: Li K 

PROVIDER: S-EPMC5477671 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature