Publication: Model Selection and Mixed-Effects Modeling of HIV Infection Dynamics
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Model Selection and Mixed-Effects Modeling of HIV Infection Dynamics

- Article in a journal -
 

Area
Biomedicine

Author(s)
D. M. Bortz , P. W. Nelson

Published in
Bulletin of Mathematical Biology

Year
2006

Abstract
We present an introduction to a model selection methodology and an application to mathematical models of in vivo HIV infection dynamics. We consider six previously published deterministic models and compare them with respect to their ability to represent HIV-infected patients undergoing reverse transcriptase mono-therapy. In the creation of the statistical model, a hierarchical mixed-effects modeling approach is employed to characterize the inter- and intra-individual variability in the patient population. We estimate the population parameters in a maximum likelihood function formulation, which is then used to calculate information theory based model selection criteria, providing a ranking of the abilities of the various models to represent patient data. The parameter fits generated by these models, furthermore, provide statistical support for the higher viral clearance rate c in Louie et al. [AIDS 17:1151--1156, 2003]. Among the candidate models, our results suggest which mathematical structures, e.g., linear versus nonlinear, best describe the data we are modeling and illustrate a framework for others to consider when modeling infectious diseases.

AD Tools
ADiMat, ADOL-C

BibTeX
@ARTICLE{
         Bortz2006MSa,
       author = "Bortz, D. M. and Nelson, P. W.",
       title = "Model Selection and Mixed-Effects Modeling of {HIV} Infection Dynamics",
       journal = "Bulletin of Mathematical Biology",
       year = "2006",
       volume = "68",
       number = "8",
       pages = "2005--2025",
       abstract = "We present an introduction to a model selection methodology and an application to
         mathematical models of in vivo HIV infection dynamics. We consider six previously published
         deterministic models and compare them with respect to their ability to represent HIV-infected
         patients undergoing reverse transcriptase mono-therapy. In the creation of the statistical model, a
         hierarchical mixed-effects modeling approach is employed to characterize the inter- and
         intra-individual variability in the patient population. We estimate the population parameters in a
         maximum likelihood function formulation, which is then used to calculate information theory based
         model selection criteria, providing a ranking of the abilities of the various models to represent
         patient data. The parameter fits generated by these models, furthermore, provide statistical support
         for the higher viral clearance rate c in Louie et al. [AIDS 17:1151--1156, 2003]. Among the
         candidate models, our results suggest which mathematical structures, e.g., linear versus nonlinear,
         best describe the data we are modeling and illustrate a framework for others to consider when
         modeling infectious diseases.",
       issn = "1522-9602",
       doi = "10.1007/s11538-006-9084-x",
       url = "http://dx.doi.org/10.1007/s11538-006-9084-x",
       ad_area = "Biomedicine",
       ad_tools = "ADiMat, ADOL-C"
}


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