Penalized Nonlinear Mixed Effects Model to Identify Biomarkers That Predict Disease Progression

• Published in: Biometrics Vol. 73; no. 4; pp. 1343 - 1354
• Main Authors: Chen, Huaihou, Zeng, Donglin, Wang, Yuanjia
• Format: Journal Article
• Language: English
• Published: United States Wiley-Blackwell 01.12.2017; Blackwell Publishing Ltd
• Subjects: algorithms; Atrophy; Biomarkers; BIOMETRIC PRACTICE; biometry; Brain; Clinical outcomes; cognition; Cognitive ability; Computational neuroscience; disease course; disease occurrence; EM algorithm; Huntington's disease; Huntingtons disease; Inflection point; Inflection points; Mathematical models; Medical imaging; Neostriatum; Neurodegenerative diseases; Neuroimaging; Neuroimaging biomarkers; Neurology; Nonlinear mixed model; nonlinear models; prediction; Predictions; Sigmoid function; Trajectories; Variable selection; Ventricle; Sigmoid function; Inflection point; EM algorithm; Nonlinear mixed model; Neuroimaging biomarkers; Variable selection
• ISSN: 0006-341X; 1541-0420
• DOI: 10.1111/biom.12663

Precise modeling of disease progression in neurodegenerative disorders may enable early intervention before clinical manifestation of a disease, which is crucial since early intervention at the premanifest stage is expected to be more effective. Neuroimaging biomarkers are indicative of the underlyi...