title: Bayesian experimental design for nonlinear mixed-effects models with application to HIV dynamics.
abstract: Bayesian experimental design is investigated for Bayesian analysis of nonlinear mixed-effects models. Existence of the posterior risk for parameter estimation is shown. When the same prior distribution is used for both design and inference, existence of the preposterior risk for design is also proven. If the prior distribution used in design is different from that used for inference, sufficient conditions are established for existence of the preposterior risk for design. A case study of design for an experiment in population HIV dynamics is provided.
authors.affiliation: TAP Pharmaceutical Products Inc., Lake Forest, Illinois 60045, USA. cong.han@tap.com
authors.forename: Cong
authors.initials: C
authors.lastname: Han
authors.forename: Kathryn
authors.initials: K
authors.lastname: Chaloner
mesh_terms: D001499:Bayes Theorem; D001699:Biometry; D015658:HIV Infections / Q000188:drug therapy / Q000821:virology*; D017320:HIV Protease Inhibitors / Q000627:therapeutic use; D006801:Humans; D008954:Models, Biological; D017711:Nonlinear Dynamics
chemical_list: D017320:HIV Protease Inhibitors