BAYESIAN INDIVIDUALIZATION VIA SAMPLING-BASED METHODS

We consider the situation where we wish to adjust the dosage regimen o f a patient based on (in general) sparse concentration measurements ta ken on-line. A Bayesian decision theory approach is taken which requir es the specification of an appropriate prior distribution and loss fun ction. A simple method for obtaining samples from the posterior distri bution of the pharmacokinetic parameters of the patient is described. In general, these samples are used to obtain a Monte Carlo estimate of the expected loss which is then minimized with respect to the dosage regimen. Some special cases which yield analytic solutions are describ ed. When the prior distribution is based on a population analysis then a method of accounting for the uncertainty in the population paramete rs is described. Two simulation studies showing how the methods work i s practice are presented.