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The International Journal of Biostatistics

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

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1557-4679
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Bayesian inference for a nonlinear mixed-effects Tobit model with multivariate skew-t distributions: application to AIDS studies

Getachew Dagne / Yangxin Huang
Published Online: 2012-09-18 | DOI: https://doi.org/10.1515/1557-4679.1387

Abstract

Censored data are characteristics of many bioassays in HIV/AIDS studies where assays may not be sensitive enough to determine gradations in viral load determination among those below a detectable threshold. Not accounting for such left-censoring appropriately can lead to biased parameter estimates in most data analysis. To properly adjust for left-censoring, this paper presents an extension of the Tobit model for fitting nonlinear dynamic mixed-effects models with skew distributions. Such extensions allow one to specify the conditional distributions for viral load response to account for left-censoring, skewness and heaviness in the tails of the distributions of the response variable. A Bayesian modeling approach via Markov Chain Monte Carlo (MCMC) algorithm is used to estimate model parameters. The proposed methods are illustrated using real data from an HIV/AIDS study.

Keywords: Bayesian method; nonlinear HIV dynamics; Tobit model; skew-normal distribution

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Published Online: 2012-09-18


Citation Information: The International Journal of Biostatistics, ISSN (Online) 1557-4679, DOI: https://doi.org/10.1515/1557-4679.1387.

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©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston. Copyright Clearance Center

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[1]
Hanze Zhang, Yangxin Huang, Wei Wang, Henian Chen, and Barbara Langland-Orban
Statistical Methods in Medical Research, 2017, Page 096228021773085
[2]
Getachew A. Dagne
Computational Statistics, 2016, Volume 31, Number 1, Page 121

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