Document Detail


Linear and Nonlinear Mixed-Effects Models for Censored HIV Viral Loads Using Normal/Independent Distributions.
MedLine Citation:
PMID:  21504417     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Summary HIV RNA viral load measures are often subjected to some upper and lower detection limits depending on the quantification assays. Hence, the responses are either left or right censored. Linear (and nonlinear) mixed-effects models (with modifications to accommodate censoring) are routinely used to analyze this type of data and are based on normality assumptions for the random terms. However, those analyses might not provide robust inference when the normality assumptions are questionable. In this article, we develop a Bayesian framework for censored linear (and nonlinear) models replacing the Gaussian assumptions for the random terms with normal/independent (NI) distributions. The NI is an attractive class of symmetric heavy-tailed densities that includes the normal, Student's-t, slash, and the contaminated normal distributions as special cases. The marginal likelihood is tractable (using approximations for nonlinear models) and can be used to develop Bayesian case-deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated with two HIV AIDS studies on viral loads that were initially analyzed using normal (censored) mixed-effects models, as well as simulations.
Authors:
Victor H Lachos; Dipankar Bandyopadhyay; Dipak K Dey
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-4-19
Journal Detail:
Title:  Biometrics     Volume:  -     ISSN:  1541-0420     ISO Abbreviation:  -     Publication Date:  2011 Apr 
Date Detail:
Created Date:  2011-4-20     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
© 2011, The International Biometric Society.
Affiliation:
Department of Statistics, Universidade Estadual de Campinas, Campinas, Sao Paulo 6065, Brazil Division of Biostatistics and Epidemiology, Medical University of South Carolina, Charleston, South Carolina 29425, U.S.A. Department of Statistics, University of Connecticut, Storrs, Connecticut 06269, U.S.A.
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