Bayesian quantile regression-based nonlinear mixed-effects joint models for time-to-event and longitudinal data with multiple features

In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load. Meanwhile, it is critical to consider various data features of repeated measurements, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution. In this research, we first establish a Bayesian joint models that accounts for all these data features simultaneously in the framework of quantile regression-based partially linear mixed-effects models. The proposed models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also conducted to assess the performance of the proposed methods under different scenarios.

Download Full-text

A Bayesian mixture of semiparametric mixed-effects joint models for skewed-longitudinal and time-to-event data

Statistics in Medicine ◽

10.1002/sim.6517 ◽

2015 ◽

Vol 34 (20) ◽

pp. 2820-2843 ◽

Cited By ~ 3

Author(s):

Jiaqing Chen ◽

Yangxin Huang

Keyword(s):

Mixed Effects ◽

Event Data ◽

Time To Event ◽

Joint Models ◽

Time To Event Data ◽

Bayesian Mixture

Download Full-text

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

Journal of Probability and Statistics ◽

10.1155/2012/614102 ◽

2012 ◽

Vol 2012 ◽

pp. 1-19 ◽

Cited By ~ 6

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.

Download Full-text