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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Robust Joint Non-linear Mixed-Effects Models and Diagnostics for Censored HIV Viral Loads with CD4 Measurement Error

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Author(s):
Bandyopadhyay, Dipankar [1] ; Castro, Luis M. [2] ; Lachos, Victor H. [3] ; Pinheiro, Hildete P. [3]
Total Authors: 4
Affiliation:
[1] Univ Minnesota, Sch Publ Hlth, Biostat, Minneapolis, MN 55455 - USA
[2] Univ Concepcion, Dept Estadist, Concepcion - Chile
[3] Univ Estadual Campinas, Dept Estat, Campinas, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS; v. 20, n. 1, p. 121-139, MAR 2015.
Web of Science Citations: 7
Abstract

Despite technological advances in efficiency enhancement of quantification assays, biomedical studies on HIV RNA collect viral load responses that are often subject to detection limits. Moreover, some related covariates such as CD4 cell count may be often measured with errors. Censored non-linear mixed-effects models are routinely used to analyze this type of data and are based on normality assumptions for the between-subject and within-subject random terms. However, derived inference may not be robust when the underlying normality assumptions are questionable, especially in presence of skewness and heavy tails. In this article, we address these issues simultaneously under a Bayesian paradigm through joint modeling of the response and covariate processes using an attractive class of skew-normal independent densities. The methodology is illustrated using a case study on longitudinal HIV viral loads. Diagnostics for outlier detection is immediate from the MCMC output. Both simulation and real data analysis reveal the advantage of the proposed models in providing robust inference under non-normality situations commonly encountered in HIV/AIDS or other clinical studies. Supplementary materials accompanying this paper appear on-line. (AU)

FAPESP's process: 12/19445-0 - Flexible modeling of complex longitudinal data using skew-elliptical distributions
Grantee:Víctor Hugo Lachos Dávila
Support Opportunities: Research Grants - Visiting Researcher Grant - International
FAPESP's process: 14/02938-9 - Estimation and diagnostics for censored mixed effects models using scale mixtures of skew-normal distributions
Grantee:Víctor Hugo Lachos Dávila
Support Opportunities: Regular Research Grants