Simultaneous Bayesian inference on a finite mixture of mixed-effects Tobit joint models for longitudinal data with multiple features

2017 ◽  
Vol 10 (4) ◽  
pp. 557-573
Author(s):  
Yangxin Huang ◽  
Jiaqing Chen ◽  
Ping Yin ◽  
Huahai Qiu
Keyword(s):  
Bayesian Inference ◽  
Longitudinal Data ◽  
Mixed Effects ◽  
Finite Mixture ◽  
Joint Models ◽  
Multiple Features

scholarly journals Mixed-Effects Tobit Joint Models for Longitudinal Data with Skewness, Detection Limits, and Measurement Errors

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Getachew A. Dagne ◽  
Yangxin Huang
Keyword(s):  
Detection Limit ◽  
Longitudinal Data ◽  
Normal Distribution ◽  
Measurement Errors ◽  
Real Data ◽  
Mixed Effects ◽  
Parameter Estimates ◽  
Joint Models ◽  
Departure From Normality ◽  
Invalid Inference

Complex longitudinal data are commonly analyzed using nonlinear mixed-effects (NLME) models with a normal distribution. However, a departure from normality may lead to invalid inference and unreasonable parameter estimates. Some covariates may be measured with substantial errors, and the response observations may also be subjected to left-censoring due to a detection limit. Inferential procedures can be complicated dramatically when such data with asymmetric characteristics, left censoring, and measurement errors are analyzed. There is relatively little work concerning all of the three features simultaneously. In this paper, we jointly investigate a skew-tNLME Tobit model for response (with left censoring) process and a skew-tnonparametric mixed-effects model for covariate (with measurement errors) process under a Bayesian framework. A real data example is used to illustrate the proposed methods.

scholarly journals Mixture Joint Models for Event Time and Longitudinal Data With Multiple Features

2016 ◽  
Vol 8 (2) ◽  
pp. 194-206 ◽  
Author(s):  
Yangxin Huang ◽  
Getachew A. Dagne ◽  
Jeong-Gun Park
Keyword(s):  
Longitudinal Data ◽  
Joint Models ◽  
Multiple Features ◽  
Event Time

Bayesian joint analysis of heterogeneous- and skewed-longitudinal data and a binary outcome, with application to AIDS clinical studies

2017 ◽  
Vol 27 (10) ◽  
pp. 2946-2963 ◽  
Author(s):  
Xiaosun Lu ◽  
Yangxin Huang ◽  
Jiaqing Chen ◽  
Rong Zhou ◽  
Shuli Yu ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Latent Class ◽  
Joint Modeling ◽  
Finite Mixture ◽  
Joint Analysis ◽  
Binary Outcome ◽  
Class Membership ◽  
Joint Models ◽  
Nonlinear Mixed Effects Models ◽  
Skew Distributions

In medical studies, heterogeneous- and skewed-longitudinal data with mis-measured covariates are often observed together with a clinically important binary outcome. A finite mixture of joint models is currently used to fit heterogeneous-longitudinal data and binary outcome, in which these two parts are connected by the individual latent class membership. The skew distributions, such as skew-normal and skew-t, have shown beneficial in dealing with asymmetric data in various applications in literature. However, there has been relatively few studies concerning joint modeling of heterogeneous- and skewed-longitudinal data and a binary outcome. In this article, we propose a joint model in which a flexible finite mixture of nonlinear mixed-effects models with skew distributions is connected with binary logistic model by a latent class membership indicator. Simulation studies are conducted to assess the performance of the proposed models and method, and a real example from an AIDS clinical trial study illustrates the methodology by modeling the viral dynamics to compare potential models with different distribution specifications; the analysis results are reported.

Sign in / Sign up

Export Citation Format

Share Document