Please use this identifier to cite or link to this item: https://doi.org/10.1002/sim.1317
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dc.titleThe study of long-term HIV dynamics using semi-parametric non-linear mixed-effects models
dc.contributor.authorWu, H.
dc.contributor.authorZhang, J.-T.
dc.date.accessioned2014-10-28T05:16:01Z
dc.date.available2014-10-28T05:16:01Z
dc.date.issued2002-12-15
dc.identifier.citationWu, 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. https://doi.org/10.1002/sim.1317
dc.identifier.issn02776715
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105431
dc.description.abstractModelling 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1002/sim.1317
dc.sourceScopus
dc.subjectAIDS
dc.subjectHIV dynamics
dc.subjectLongitudinal data
dc.subjectMixed-effects models
dc.subjectSemi-parametric non-linear mixed-effects models
dc.subjectViral dynamics
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1002/sim.1317
dc.description.sourcetitleStatistics in Medicine
dc.description.volume21
dc.description.issue23
dc.description.page3655-3675
dc.description.codenSMEDD
dc.identifier.isiut000179236600008
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