A Gaussian copula joint model for longitudinal and time-to-event data with random effects
From MaRDI portal
Publication:6113736
DOI10.1016/j.csda.2022.107685arXiv2112.01941OpenAlexW4313462089MaRDI QIDQ6113736
No author found.
Publication date: 11 July 2023
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2112.01941
Related Items (1)
Cites Work
- A fast EM algorithm for fitting joint models of a binary response and multiple longitudinal covariates subject to detection limits
- Random-Effects Models for Longitudinal Data
- Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule
- An introduction to copulas.
- Faster Monte Carlo estimation of joint models for time-to-event and multivariate longitudinal data
- Joint latent class model of survival and longitudinal data: an application to CPCRA study
- Bayesian functional joint models for multivariate longitudinal and time-to-event data
- A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data
- Joint Models for Longitudinal and Time-to-Event Data
- Fully Exponential Laplace Approximations for the Joint Modelling of Survival and Longitudinal Data
- A Semiparametric Likelihood Approach to Joint Modeling of Longitudinal and Time-to-Event Data
- Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data
- Improved dynamic predictions from joint models of longitudinal and survival data with time-varying effects using P-splines
- A Two-Part Joint Model for the Analysis of Survival and Longitudinal Binary Data with Excess Zeros
- Score Test for Conditional Independence Between Longitudinal Outcome and Time to Event Given the Classes in the Joint Latent Class Model
- Shared parameter models under random effects misspecification
- Covariate measurement errors and parameter estimation in a failure time regression model
- A Joint Model for Survival and Longitudinal Data Measured with Error
- Latent Class Models for Joint Analysis of Longitudinal Biomarker and Event Process Data
- Joint modelling of longitudinal response and time-to-event data using conditional distributions: a Bayesian perspective
- Joint modelling of longitudinal biomarker and gap time between recurrent events: copula-based dependence
- A shared parameter model of longitudinal measurements and survival time with heterogeneous random-effects distribution
- A Simplex Method for Function Minimization
- Joint modelling of longitudinal measurements and event time data
- Elements of Copula Modeling with R
- Linear mixed models for longitudinal data
- Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer's Disease
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: A Gaussian copula joint model for longitudinal and time-to-event data with random effects