Longitudinal survival analysis and two-group comparison for predicting the progression of mild cognitive impairment to Alzheimer
The characterization of the progression of Alzheimer's disease (AD) is important for both early detection and treatment. Longitudinal studies using structural magnetic resonance imaging (MRI) and neuropsychological measurements (NMs) allow a noninvasive means of following the subtle anatomical changes occurring during the evolution of AD.
This project compared two approaches for the construction of longitudinal predictive models, which were used here to estimate the conversion of mild cognitive impairment (MCI) to AD. These approaches were as follows:
a) two-group comparison between converter and nonconverter MCI subjects and b) model-based
survival analysis. Predictive models combined MRI-based markers (cortical thickness and volume of subcortical structures) with NMs and included demographic and clinical information as covariates. Both approaches employed linear mixed effect (LME) modeling to capture the longitudinal trajectories of the markers.
This project compared two approaches for the construction of longitudinal predictive models, which were used here to estimate the conversion of mild cognitive impairment (MCI) to AD. These approaches were as follows:
a) two-group comparison between converter and nonconverter MCI subjects and b) model-based
survival analysis. Predictive models combined MRI-based markers (cortical thickness and volume of subcortical structures) with NMs and included demographic and clinical information as covariates. Both approaches employed linear mixed effect (LME) modeling to capture the longitudinal trajectories of the markers.
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twogrsurvana: Code matlab2018b release
demo_predictiveModels_190729.zip posted by Carlos Platero on Jul 30, 2019