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Bayesian Optimal Design for the Study of Butadiene Toxicokinetics

Billy Amzal - Frédéric Bois


As a following of the paper 'Optimal design for a study of butadiene toxicokinetics in humans' (Bois and Smith, Toxicol Sci, 1999; 49:213-24), we propose a simulation-based approach to decision theoretic optimal Bayesian design in the context of population pharmacokinetic (PK)models (repeated measurement model, random effects regression models, population models). We investigate the optimal design for the number of subjects and sampling times per subject in a study of 1,3-butadiene toxicokinetics in humans. For that purpose, we maximize, under cost restrictions, a utility function that represent the information provided by the experiments. We implement the MCMC scheme developped by Peter Müller in a recent paper. The Bayesian framework allows us to use data from previous experiments and gives us a robust method to determine a non-sequential and ready-to-use optimal design.