Bayesian inference and dynamic prediction of multivariate joint model with functional data: An application to Alzheimer's disease

2021 ◽  
Author(s):  
Haotian Zou ◽  
Kan Li ◽  
Donglin Zeng ◽  
Sheng Luo ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Bayesian Inference ◽  
Functional Data ◽  
Joint Model ◽  
Dynamic Prediction

scholarly journals Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer’s disease

2017 ◽  
Vol 28 (2) ◽  
pp. 327-342 ◽  
Author(s):  
Kan Li ◽  
Sheng Luo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Survival Data ◽  
Functional Data ◽  
Joint Modeling ◽  
Joint Model ◽  
Repeated Measurements ◽  
Joint Models ◽  
Modeling Framework ◽  
Dynamic Prediction

In the study of Alzheimer’s disease, researchers often collect repeated measurements of clinical variables, event history, and functional data. If the health measurements deteriorate rapidly, patients may reach a level of cognitive impairment and are diagnosed as having dementia. An accurate prediction of the time to dementia based on the information collected is helpful for physicians to monitor patients’ disease progression and to make early informed medical decisions. In this article, we first propose a functional joint model to account for functional predictors in both longitudinal and survival submodels in the joint modeling framework. We then develop a Bayesian approach for parameter estimation and a dynamic prediction framework for predicting the subjects’ future outcome trajectories and risk of dementia, based on their scalar and functional measurements. The proposed Bayesian functional joint model provides a flexible framework to incorporate many features both in joint modeling of longitudinal and survival data and in functional data analysis. Our proposed model is evaluated by a simulation study and is applied to the motivating Alzheimer’s Disease Neuroimaging Initiative study.

scholarly journals Hippocampal shape analysis in Alzheimer's disease using functional data analysis

2013 ◽  
Vol 33 (5) ◽  
pp. 867-880 ◽  
Author(s):  
Irene Epifanio ◽  
Noelia Ventura-Campos
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Data Analysis ◽  
Shape Analysis ◽  
Functional Data Analysis ◽  
Functional Data

Label‐noise resistant logistic regression for functional data classification with an application to Alzheimer's disease study

Biometrics ◽  
2016 ◽  
Vol 72 (4) ◽  
pp. 1325-1335 ◽  
Author(s):  
Seokho Lee ◽  
Hyejin Shin ◽  
Sang Han Lee
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Logistic Regression ◽  
Functional Data ◽  
Data Classification ◽  
Label Noise ◽  
Disease Study

scholarly journals Comparisons of neurodegeneration over time between healthy ageing and Alzheimer's disease cohorts via Bayesian inference

BMJ Open ◽  
2017 ◽  
Vol 7 (2) ◽  
pp. e012174 ◽  
Author(s):  
Marcela I Cespedes ◽  
Jurgen Fripp ◽  
James M McGree ◽  
Christopher C Drovandi ◽  
Kerrie Mengersen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Bayesian Inference ◽  
Healthy Ageing ◽  
Over Time

scholarly journals PRM24 - A JOINT MODEL OF COGNITIVE DECLINE AND TIME TO DIAGNOSIS TO IMPROVE PREDICTIONS OF THE RISK OF DEVELOPING ALZHEIMER’S DISEASE

2018 ◽  
Vol 21 ◽  
pp. S359-S360
Author(s):  
H Karcher ◽  
V Risson ◽  
G Lestini ◽  
N Coello ◽  
L Qi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Joint Model ◽  
Time To Diagnosis

scholarly journals A joint model for multiple dynamic processes and clinical endpoints: Application to Alzheimer's disease

2019 ◽  
Vol 38 (23) ◽  
pp. 4702-4717 ◽  
Author(s):  
Cécile Proust‐Lima ◽  
Viviane Philipps ◽  
Jean‐François Dartigues
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Joint Model ◽  
Dynamic Processes ◽  
Clinical Endpoints

scholarly journals Functional joint model for longitudinal and time-to-event data: an application to Alzheimer's disease

2017 ◽  
Vol 36 (22) ◽  
pp. 3560-3572 ◽  
Author(s):  
Kan Li ◽  
Sheng Luo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Joint Model ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data
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