Bayesian inference on joint models of HIV dynamics for time-to-event and longitudinal data with skewness and covariate measurement errors

2011 ◽  
Vol 30 (24) ◽  
pp. 2930-2946 ◽  
Author(s):  
Yangxin Huang ◽  
Getachew Dagne ◽  
Lang Wu
Keyword(s):  
Bayesian Inference ◽  
Longitudinal Data ◽  
Measurement Errors ◽  
Time To Event ◽  
Joint Models ◽  
Hiv Dynamics ◽  
Covariate Measurement Errors

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.

Mixed-effects location and scale Tobit joint models for heterogeneous longitudinal data with skewness, detection limits, and measurement errors

2017 ◽  
Vol 27 (12) ◽  
pp. 3525-3543
Author(s):  
Tao Lu
Keyword(s):  
Longitudinal Data ◽  
Measurement Errors ◽  
Limit Of Detection ◽  
Joint Modeling ◽  
Research Area ◽  
Joint Model ◽  
Mixed Effects ◽  
Joint Models ◽  
Active Research ◽  
Aids Study

The joint modeling of mean and variance for longitudinal data is an active research area. This type of model has the advantage of accounting for heteroscedasticity commonly observed in between and within subject variations. Most of researches focus on improving the estimating efficiency but ignore many data features frequently encountered in practice. In this article, we develop a mixed-effects location scale joint model that concurrently accounts for longitudinal data with multiple features. Specifically, our joint model handles heterogeneity, skewness, limit of detection, measurement errors in covariates which are typically observed in the collection of longitudinal data from many studies. We employ a Bayesian approach for making inference on the joint model. The proposed model and method are applied to an AIDS study. Simulation studies are performed to assess the performance of the proposed method. Alternative models under different conditions are compared.

Faster Monte Carlo estimation of joint models for time-to-event and multivariate longitudinal data

2020 ◽  
Vol 151 ◽  
pp. 107010
Author(s):  
Pete Philipson ◽  
Graeme L. Hickey ◽  
Michael J. Crowther ◽  
Ruwanthi Kolamunnage-Dona
Keyword(s):  
Monte Carlo ◽  
Longitudinal Data ◽  
Time To Event ◽  
Joint Models ◽  
Multivariate Longitudinal Data ◽  
Monte Carlo Estimation
Sign in / Sign up

Export Citation Format

Share Document