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Title: The study of long-term HIV dynamics using semi-parametric non-linear mixed-effects models
Authors: Wu, H.
Zhang, J.-T. 
Keywords: AIDS
HIV dynamics
Longitudinal data
Mixed-effects models
Semi-parametric non-linear mixed-effects models
Viral dynamics
Issue Date: 15-Dec-2002
Citation: Wu, H., Zhang, J.-T. (2002-12-15). The study of long-term HIV dynamics using semi-parametric non-linear mixed-effects models. Statistics in Medicine 21 (23) : 3655-3675. ScholarBank@NUS Repository.
Abstract: Modelling HIV dynamics has played an important role in understanding the pathogenesis of HIV infection in the past several years. Non-linear parametric models, derived from the mechanisms of HIV infection and drug action, have been used to fit short-term clinical data from AIDS clinical trials. However, it is found that the parametric models may not be adequate to fit long-term HIV dynamic data. To preserve the meaningful interpretation of the short-term HIV dynamic models as well as to characterize the long-term dynamics, we introduce a class of semi-parametric non-linear mixed-effects (NLME) models. The models are non-linear in population characteristics (fixed effects) and individual variations (random effects), both of which are modelled semi-parametrically. A basis-based approach is proposed to fit the models, which transforms a general semi-parametric NLME model into a set of standard parametric NLME models, indexed by the bases used. The bases that we employ are natural cubic splines for easy implementation. The resulting standard NLME models are low-dimensional and easy to solve. Statistical inferences that include testing parametric against semi-parametric mixed-effects are investigated. Innovative bootstrap procedures are developed for simulating the empirical distributions of the test statistics. Small-scale simulation and bootstrap studies show that our bootstrap procedures work well. The proposed approach and procedures are applied to long-term HIV dynamic data from an AIDS clinical study. Copyright © 2002 John Wiley & Sons, Ltd.
Source Title: Statistics in Medicine
ISSN: 02776715
DOI: 10.1002/sim.1317
Appears in Collections:Staff Publications

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