Joint Modeling of Longitudinal Imaging and Survival Data

DOI10.1080/10618600.2022.2102027OpenAlexW4285727445MaRDI QIDQ6047651

Kai Kang, Xin-Yuan Song

Publication date: 9 October 2023

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2022.2102027

zbMATH Keywords

MCMC methodslongitudinal responsetime-to-event outcomeimaging dataHD-FPCA

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• BFLCRM: a Bayesian functional linear Cox regression model for predicting time to conversion to Alzheimer's disease
• Analysis of longitudinal and survival data: joint modeling, inference methods, and issues
• TPRM: tensor partition regression models with applications in imaging biomarker detection
• Bayesian functional joint models for multivariate longitudinal and time-to-event data
• Longitudinal functional principal component analysis
• Longitudinal high-dimensional principal components analysis with application to diffusion tensor imaging of multiple sclerosis
• Generalized functional linear models
• A Bayesian Semiparametric Joint Hierarchical Model for Longitudinal and Survival Data
• Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data
• FLCRM: Functional linear cox regression model
• Improved dynamic predictions from joint models of longitudinal and survival data with time-varying effects using P-splines
• Joint modeling of longitudinal data with informative observation times and dropouts
• Covariate measurement errors and parameter estimation in a failure time regression model
• Jointly Modeling Longitudinal and Event Time Data With Application to Acquired Immunodeficiency Syndrome
• Latent Class Models for Joint Analysis of Longitudinal Biomarker and Event Process Data
• Functional Linear Regression Model for Nonignorable Missing Scalar Responses
• Modelling Progression of CD4-Lymphocyte Count and Its Relationship to Survival Time
• Bayesian Scalar on Image Regression With Nonignorable Nonresponse
• Equation of State Calculations by Fast Computing Machines
• Tensor Regression with Applications in Neuroimaging Data Analysis
• Scalar-on-image regression via the soft-thresholded Gaussian process
• Selecting the Number of Principal Components in Functional Data
• Monte Carlo sampling methods using Markov chains and their applications
• Survival Model Predictive Accuracy and ROC Curves

This page was built for publication: Joint Modeling of Longitudinal Imaging and Survival Data