Bayesian functional joint models for multivariate longitudinal and time-to-event data

DOI10.1016/j.csda.2018.07.015zbMath1469.62103OpenAlexW2885740872WikidataQ90597827 ScholiaQ90597827MaRDI QIDQ1799810

Kan Li, Sheng Luo

Publication date: 19 October 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc6294314

zbMATH Keywords

Alzheimer's diseasejoint modelingdynamic predictionlongitudinal functional data

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (4)

Joint modeling of longitudinal continuous, longitudinal ordinal, and time-to-event outcomes ⋮ Joint modelling of longitudinal response and time-to-event data using conditional distributions: a Bayesian perspective ⋮ Joint Modeling of Longitudinal Imaging and Survival Data ⋮ A Gaussian copula joint model for longitudinal and time-to-event data with random effects

Uses Software

• WinBUGS
• SemiPar
• BayesDA
• FSL
• NUTS

Cites Work

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• Functional Mixed Effects Models
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• Wavelet-based Functional Mixed Models
• Generalized multilevel function‐on‐scalar regression and principal component analysis
• Adaptive Rejection Metropolis Sampling within Gibbs Sampling
• Semiparametric Regression
• Dynamic Latent Trait Models for Multidimensional Longitudinal Data
• Smoothing Spline Models for the Analysis of Nested and Crossed Samples of Curves
• Joint modelling of longitudinal measurements and event time data
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)

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