Linked Life Data

11318194

Source:http://linkedlifedata.com/resource/pubmed/id/11318194

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2001-4-24
pubmed:abstractText
In this paper, we introduce a novel application of hierarchical nonlinear mixed-effect models to HIV dynamics. We show that a simple model with a sum of exponentials can give a good fit to the observed clinical data of HIV-1 dynamics (HIV-1 RNA copies) after initiation of potent antiviral treatments and can also be justified by a biological compartment model for the interaction between HIV and its host cells. This kind of model enjoys both biological interpretability and mathematical simplicity after reparameterization and simplification. A model simplification procedure is proposed and illustrated through examples. We interpret and justify various simplified models based on clinical data taken during different phases of viral dynamics during antiviral treatments. We suggest the hierarchical nonlinear mixed-effect model approach for parameter estimation and other statistical inferences. In the context of an AIDS clinical trial involving patients treated with a combination of potent antiviral agents, we show how the models may be used to draw biologically relevant interpretations from repeated HIV-1 RNA measurements and demonstrate the potential use of the models in clinical decision-making.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0006-341X
pubmed:author
pubmed:issnType
Print
pubmed:volume
55
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
410-8
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
Population HIV-1 dynamics in vivo: applicable models and inferential tools for virological data from AIDS clinical trials.
pubmed:affiliation
Statistical and Data Analysis Center, Harvard School of Public Health, Frontier Science and Technology Research Foundation, Inc., Chestnut Hill, Massachusetts 02467, USA. wu@sdac.harvard.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.