Jump to ContentJump to Main Navigation
Show Summary Details
More options …

The International Journal of Biostatistics

Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.

2 Issues per year

IMPACT FACTOR 2017: 0.840
5-year IMPACT FACTOR: 1.000

CiteScore 2017: 0.97

SCImago Journal Rank (SJR) 2017: 1.150
Source Normalized Impact per Paper (SNIP) 2017: 1.022

Mathematical Citation Quotient (MCQ) 2016: 0.09

See all formats and pricing
More options …

Mixed-Effects Joint Models with Skew-Normal Distribution for HIV Dynamic Response with Missing and Mismeasured Time-Varying Covariate

Yangxin Huang / Jiaqing Chen / Chunning Yan
Published Online: 2012-11-26 | DOI: https://doi.org/10.1515/1557-4679.1426


Longitudinal data arise frequently in medical studies and it is a common practice to analyze such complex data with nonlinear mixed-effects (NLME) models, which enable us to account for between-subject and within-subject variations. To partially explain the variations, time-dependent covariates are usually introduced to these models. Some covariates, however, may be often measured with substantial errors and missing observations. It is often the case that model random error is assumed to be distributed normally, but the normality assumption may not always give robust and reliable results, particularly if the data exhibit skewness. In the literature, there has been considerable interest in accommodating either skewed response or covariate measured with error and missing data in such models, but there has been relatively little study concerning all these features simultaneously. This article is to address simultaneous impact of skewness in response and measurement error and missing data in covariate by jointly modeling the response and covariate processes under a framework of Bayesian semiparametric nonlinear mixed-effects models. In particular, we aim at exploring how mixed-effects joint models based on one-compartment model with one phase time-varying decay rate and two-compartment model with two phase time-varying decay rates contribute to modeling results and inference. The method is illustrated by an AIDS data example to compare potential models with different distributional specifications and various scenarios. The findings from this study suggest that the one-compartment model with a skew-normal distribution may provide more reasonable results if the data exhibit skewness in response and/or have measurement error and missing observations in covariates.

Keywords: Bayesian analysis; HIV dynamics; measurement error; missing covariate; mixed-effects joint models; skew-normal distribution

About the article

Published Online: 2012-11-26

Citation Information: The International Journal of Biostatistics, Volume 8, Issue 1, ISSN (Online) 1557-4679, DOI: https://doi.org/10.1515/1557-4679.1426.

Export Citation

©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Larissa A. Matos, Luis M. Castro, Celso R. B. Cabral, and Víctor H. Lachos
Statistics, 2018, Page 1
Yangxin Huang and Tao Lu
Computational Statistics, 2017, Volume 32, Number 1, Page 179
Yangxin Huang, Getachew A. Dagne, and Jeong-Gun Park
Statistics in Biopharmaceutical Research, 2016, Volume 8, Number 2, Page 194
Xiaosun Lu, Yangxin Huang, and Yiliang Zhu
Computational Statistics & Data Analysis, 2016, Volume 93, Page 119
Xiaosun Lu and Yangxin Huang
Statistics in Medicine, 2014, Volume 33, Number 16, Page 2830

Comments (0)

Please log in or register to comment.
Log in