A Bayesian semi-parametric model for learning biomarker trajectories and changepoints in the preclinical phase of Alzheimer's disease.

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第一作者： Kunbo,Wang

第一单位： Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, United States.

作者： Kunbo,Wang ^[1] ; William,Hua ^[1] ; MeiCheng,Wang ^[2] ; Yanxun,Xu ^[1]

作者单位： Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, United States. ^[1] Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, United States. ^[2]

关键词 Alzheimer’s disease BIOCARD study Bayesian semiparametrics changepoint detection left truncation

主题词阿尔茨海默病(Alzheimer Disease);贝叶斯定理(Bayes Theorem);人类(Humans);疾病恶化(Disease Progression);计算机模拟(Computer Simulation);模型, 统计学(Models, Statistical);tau蛋白质类(tau Proteins);纵向研究(Longitudinal Studies)

DOI 10.1093/biomtc/ujae048

PMID 38775703

发布时间 2024-05-24

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2024年80卷2期

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A Bayesian semi-parametric model for learning biomarker trajectories and changepoints in the preclinical phase of Alzheimer's disease.

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A Bayesian semi-parametric model for learning biomarker trajectories and changepoints in the preclinical phase of Alzheimer's disease.

Biometrics

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