scholarly journals Dynamic prediction of Alzheimer's disease progression using features of multiple longitudinal outcomes and time‐to‐event data

2019 ◽  
Vol 38 (24) ◽  
pp. 4804-4818 ◽  
Author(s):  
Kan Li ◽  
Sheng Luo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Event Data ◽  
Time To Event ◽  
Dynamic Prediction ◽  
Longitudinal Outcomes ◽  
Time To Event Data

scholarly journals Prediction of Conversion to Alzheimer’s Disease with Longitudinal Measures and Time-To-Event Data

2017 ◽  
Vol 58 (2) ◽  
pp. 361-371 ◽  
Author(s):  
Kan Li ◽  
Wenyaw Chan ◽  
Rachelle S. Doody ◽  
Joseph Quinn ◽  
Sheng Luo ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

An Overview of Joint Modeling of Time-to-Event and Longitudinal Outcomes

2019 ◽  
Vol 6 (1) ◽  
pp. 223-240 ◽  
Author(s):  
Grigorios Papageorgiou ◽  
Katya Mauff ◽  
Anirudh Tomer ◽  
Dimitris Rizopoulos
Keyword(s):  
Joint Modeling ◽  
Joint Model ◽  
Event Data ◽  
Time To Event ◽  
Joint Models ◽  
Longitudinal Outcomes ◽  
Estimation Techniques ◽  
Time To Event Data ◽  
Association Structure

In this review, we present an overview of joint models for longitudinal and time-to-event data. We introduce a generalized formulation for the joint model that incorporates multiple longitudinal outcomes of varying types. We focus on extensions for the parametrization of the association structure that links the longitudinal and time-to-event outcomes, estimation techniques, and dynamic predictions. We also outline the software available for the application of these models.

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