THE HIERARCHICAL BAYESIAN-APPROACH TO POPULATION PHARMACOKINETIC MODELING

Compartmental models are widely used to model the profile of drug conc entrations versus time from administration in an individual subject. O bserved concentrations are then modelled as noisy departures from the underlying profile, the latter characterised for each individual by a small number of 'individual parameters'. When a population of individu als is studied, inter-individual variation is modelled by assuming tha t the individual profile parameters are drawn from a population distri bution, the latter characterised by 'population parameters' describing , in effect, a mean population profile and individual variation around it. From a Bayesian statistical perspective, such models fit exactly into the so-called hierarchical modelling framework, which provides a coherent basis for individual and population inferences and prediction , as well. as for decision-making (for example, the design of dosage r egimens). This paper outlines the hierarchical model framework and des cribes how the required computations can be carried out in a straightf orward manner by a Markov chain Monte Carlo technique known as Gibbs s ampling, even when models involve mean-variance relationships and outl iers.