title: Designing an optimal experiment for Bayesian estimation: application to the kinetics of iodine thyroid uptake.
abstract: We consider the problem of designing an optimal experiment for Bayesian estimation of the parameters of a non-linear model. When their distribution is known, the Bayesian approach allows individual estimation from a small number of measurements; the design determines the accuracy of the estimates. We propose to optimize this design by maximizing a general criterion: the expectation of the information supplied by the experiment. This approach is applied to optimize the two sampling times for Bayesian estimation of the kinetics of radioiodine thyroid uptake from an estimated non-parametric prior distribution.
authors.affiliation: INSERM U194, GERC, Service d'Informatique Médicale, CHU Pitié-Salpétriêre, Paris, France.
authors.forename: Y
authors.initials: Y
authors.lastname: Merlé
authors.forename: F
authors.initials: F
authors.lastname: Mentré
authors.forename: A
authors.initials: A
authors.lastname: Mallet
authors.forename: A H
authors.initials: AH
authors.lastname: Aurengo
mesh_terms: D001499:Bayes Theorem; D005260:Female; D006111:Graves Disease / Q000532:radiotherapy; D006801:Humans; D007457:Iodine Radioisotopes / Q000493:pharmacokinetics* / Q000627:therapeutic use; D007700:Kinetics; D008297:Male; D008954:Models, Biological; D017711:Nonlinear Dynamics; D012189:Retrospective Studies; D013961:Thyroid Gland / Q000378:metabolism; D014018:Tissue Distribution
chemical_list: D007457:Iodine Radioisotopes