A prognostic model of Alzheimer's disease relying on multiple longitudinal measures and time-to-event data.

ABSTRACT:

Introduction

Characterizing progression in Alzheimer's disease is critically important for early detection and targeted treatment. The objective was to develop a prognostic model, based on multivariate longitudinal markers, for predicting progression-free survival in patients with mild cognitive impairment.

Methods

The information contained in multiple longitudinal markers was extracted using multivariate functional principal components analysis and used as predictors in the Cox regression models. Cross-validation was used for selecting the best model based on Alzheimer's Disease Neuroimaging Initiative-1. External validation was conducted on Alzheimer's Disease Neuroimaging Initiative-2.

Results

Model comparison yielded a prognostic index computed as the weighted com

SUBMITTER: Li K 

PROVIDER: S-EPMC5938096 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature