Retracted: Bayesian inference on mixed-effects location scale models with skew-t distribution and mismeasured covariates for longitudinal data

2017 ◽  
Vol 36 (16) ◽  
pp. 2614-2629 ◽  
Author(s):  
Tao Lu
Keyword(s):  
Bayesian Inference ◽  
Longitudinal Data ◽  
Mixed Effects ◽  
Scale Models ◽  
T Distribution

Bayesian inference on mixed-effects varying-coefficient joint models with skew-t distribution for longitudinal data with multiple features

2015 ◽  
Vol 26 (3) ◽  
pp. 1146-1164 ◽  
Author(s):  
Tao Lu ◽  
Yangxin Huang
Keyword(s):  
Clinical Study ◽  
Bayesian Inference ◽  
Joint Model ◽  
Mixed Effects ◽  
Time Varying ◽  
Varying Coefficient ◽  
Cell Counts ◽  
Cd4 Cell Counts ◽  
Cd4 Cell ◽  
T Distribution

In AIDS clinical study, two biomarkers, HIV viral load and CD4 cell counts, play important roles. It is well known that there is inverse relationship between the two. Nevertheless, the relationship is not constant but time varying. The mixed-effects varying-coefficient model is capable of capturing the time varying nature of such relationship from both population and individual perspective. In practice, the nucleic acid sequence-based amplification assay is used to measure plasma HIV-1 RNA with a limit of detection (LOD) and the CD4 cell counts are usually measured with much noise and missing data often occur during the treatment. Furthermore, most of the statistical models assume symmetric distribution, such as normal, for the response variables. Often time, normality assumption does not hold in practice. Therefore, it is important to explore all these factors when modeling the real data. In this article, we establish a joint model that accounts for asymmetric and LOD data for the response variable, and covariate measurement error and missingness simultaneously in the mixed-effects varying-coefficient modeling framework. A Bayesian inference procedure is developed to estimate the parameters in the joint model. The proposed model and method are applied to a real AIDS clinical study and various comparisons of a few models are performed.

scholarly journals Joint mixed-effects models for causal inference with longitudinal data

2017 ◽  
Vol 37 (5) ◽  
pp. 829-846 ◽  
Author(s):  
Michelle Shardell ◽  
Luigi Ferrucci
Keyword(s):  
Longitudinal Data ◽  
Causal Inference ◽  
Mixed Effects ◽  
Mixed Effects Models
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