title: Model selection and mixed-effects modeling of HIV infection dynamics.
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.
authors.affiliation: Mathematical Biology Research Group, Department of Mathematics, University of Michigan, Ann Arbor, MI 48109-1043, USA. dmbortz@umich.edu
authors.forename: D M
authors.initials: DM
authors.lastname: Bortz
authors.forename: P W
authors.initials: PW
authors.lastname: Nelson
mesh_terms: D018791:CD4 Lymphocyte Count; D015658:HIV Infections / Q000188:drug therapy* / Q000821:virology*; D015497:HIV-1 / Q000235:genetics / Q000254:growth & development*; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D012367:RNA, Viral / Q000097:blood; D018894:Reverse Transcriptase Inhibitors / Q000627:therapeutic use*
chemical_list: D012367:RNA, Viral; D018894:Reverse Transcriptase Inhibitors