Effect of the number of samples on Bayesian and non-linear least-squares individualization: A study of cyclosporin treatment of haematological patients with multidrug resistance

We have studied whether the prediction of drug concentrations improves as t he number of samples used for individualization is increased, and whether t he Bayesian method of individualization is superior to the non-linear least -squares method. Data were obtained from ten adult haematological patients with multidrug resistance who were treated with cyclosporin. The prediction s of blood-cyclosporin concentrations were made using the Abbott PKS progra m, The number of samples used for individualization was increased from 1 to 30 for the Bayesian method and from 4 to 30 for the non-linear least-squar es method, Linear regression, percentage prediction error, and absolute and relative predictive performance were used to evaluate the predictions. The results show that the Bayesian method affords greater precision than th e non-linear least-squares method, but that the non-linear least-squares me thod is more accurate and results in less bias. Whereas for linear regressi on predictions improve as the number of samples is increased, other evaluat ions show improvement in the range from 5 to 11 samples; linear regression, percentage prediction errors and prediction bias support the opinion that the Bayesian method progressively becomes the non-linear least-squares meth od as the number of samples used for individualization is increased, but th e accuracy and precision of prediction do not support this opinion. The study supports the statement that Bayes' law requires parameters from a n infinite population, otherwise the advantage of the Bayesian method might be marginal.