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

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 mi...

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Bibliographic Details
Published inJournal of agricultural, biological, and environmental statistics Vol. 20; no. 1; pp. 121 - 139
Main Authors Bandyopadhyay, Dipankar, Castro, Luis M., Lachos, Victor H., Pinheiro, Hildete P.
Format Journal Article
LanguageEnglish
Published Boston Springer Science+Business Media, LLC 01.03.2015
Springer US
Springer
Subjects
Agriculture
Analysis
Biostatistics
Case studies
detection limit
diagnostic techniques
Health Sciences
HIV (Viruses)
HIV testing
Human immunodeficiency virus
Innovations
longitudinal studies
Mathematics and Statistics
Measurement
Medicine
Monitoring/Environmental Analysis
RNA
Statistics
Statistics for Life Sciences
viral load
MCMC
Skew-normal independent distributions
Case-deletion diagnostics
Bayesian
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Summary: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.
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ISSN:1085-7117
1537-2693
DOI:10.1007/s13253-014-0195-9