Liu, W.; Li, H.; Tang, A.; Cui, Z. Bayesian Joint Modeling Analysis of Longitudinal Proportional and Survival Data. Mathematics2023, 11, 3469.
Liu, W.; Li, H.; Tang, A.; Cui, Z. Bayesian Joint Modeling Analysis of Longitudinal Proportional and Survival Data. Mathematics 2023, 11, 3469.
Liu, W.; Li, H.; Tang, A.; Cui, Z. Bayesian Joint Modeling Analysis of Longitudinal Proportional and Survival Data. Mathematics2023, 11, 3469.
Liu, W.; Li, H.; Tang, A.; Cui, Z. Bayesian Joint Modeling Analysis of Longitudinal Proportional and Survival Data. Mathematics 2023, 11, 3469.
Abstract
This paper focuses on a joint model to analyse longitudinal proportion and survival data. After transforming the longitudinal proportional data by a logit function, we adopt a partially linear mixed-effect model for it, in which nonlinear function of time is fitted via using B-splines technique and a centered Dirichlet Process Mixture Model (CDPMM) is specified for a general distribution of random effects. The survival data is assumed to a Cox proportional hazard model, the sharing random effects joint model is developed for the two types of data. Combining the Gibbs sampler and the Metropolis-Hastings algorithm, we propose a Bayesian Lasso (BLasso) method to simultaneously estimate unknown parameters and select important covariates. Simulation studies are conducted to investigate the finite sample performance of the proposed methods. An example from the MA.5 research experiment is used to illustrate the proposed methodologies.
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
