Characterizing heterogeneity in Alzheimer's disease progression: a semiparametric model.

ABSTRACT: The progression of Alzheimer's disease (AD), a leading cause of dementia worldwide, is known for its variability and complexity, challenging the conventional methods of monitoring and predicting disease trajectories. This study introduces a semiparametric modeling approach to analyze longitudinal cognitive and imaging data. We studied two different outcome variables from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database: the Alzheimer's Disease Assessment Scale-Cognitive Subscale 13 (ADAS13) scores and ventricular volumes [Formula: see text]. Unlike traditional linear mixed effects models, semiparametric models do not assume a linear AD progression over time. Semiparametric models offer the advantage of capturing the non-linear features of AD progression, such as cognitive de

SUBMITTER: Gelir F 

PROVIDER: S-EPMC11880422 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature