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 Joint Models of Longitudinal Outcomes and Informative Time

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
JangDong Seo
Keyword(s):  
Longitudinal Data ◽  
Real Data ◽  
Parameter Estimates ◽  
Data Sets ◽  
Joint Models ◽  
Time Intervals ◽  
Asymptotic Behaviors ◽  
Longitudinal Outcomes ◽  
Data Analyses

Longitudinal data analyses commonly assume that time intervals are predetermined and have no information regarding the outcomes. However, there might be irregular time intervals and informative time. Presented are joint models and asymptotic behaviors of the parameter estimates. Also, the models are applied for real data sets.

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.

scholarly journals Bayesian semiparametric modeling for HIV longitudinal data with censoring and skewness

2018 ◽  
Vol 28 (5) ◽  
pp. 1457-1476 ◽  
Author(s):  
Luis M Castro ◽  
Wan-Lun Wang ◽  
Victor H Lachos ◽  
Vanda Inácio de Carvalho ◽  
Cristian L Bayes
Keyword(s):  
Longitudinal Data ◽  
Normal Distribution ◽  
Simulated Data ◽  
Real Data ◽  
Skew Normal Distribution ◽  
Individual Subject ◽  
Bayesian Semiparametric ◽  
Nonlinear Relation ◽  
The Individual ◽  
Skew Normal

In biomedical studies, the analysis of longitudinal data based on Gaussian assumptions is common practice. Nevertheless, more often than not, the observed responses are naturally skewed, rendering the use of symmetric mixed effects models inadequate. In addition, it is also common in clinical assays that the patient’s responses are subject to some upper and/or lower quantification limit, depending on the diagnostic assays used for their detection. Furthermore, responses may also often present a nonlinear relation with some covariates, such as time. To address the aforementioned three issues, we consider a Bayesian semiparametric longitudinal censored model based on a combination of splines, wavelets, and the skew-normal distribution. Specifically, we focus on the use of splines to approximate the general mean, wavelets for modeling the individual subject trajectories, and on the skew-normal distribution for modeling the random effects. The newly developed method is illustrated through simulated data and real data concerning AIDS/HIV viral loads.

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