Abstract
Several statistical and empirical approaches have been proposed to select the multiexponential equation that best describes the time course of the plasma concentration of a drug. Recently, a new criterion (I~p~) has been proposed according to which the model that best interprets a set of experimental data points is the one with the smallest area between the approximate confidence limits of estimated plasma concentration. We used large Montecarlo simulations to compare the ability of different selection criteria to select the correct model from data generated with an independent, normally distributed random error. The new criterion (I~p~), Akaike's information criterion, the Schwartz test, and the F ratio test were studied. In this situation, the correct model was known and the performances of different selecting methods were assessed by examining their sensitivity to the number of exponential terms, the number of data points, and the size of the exponents in the true model. Monoβ, biβ, and triexponential equations were studied. Overall mean percentages of right identification were 98Β·1 per cent for the new index, 82.8 per cent for Akaike's information criterion, 89.5 per cent for the Schwartz test, and 97.7 per cent for the F ratio test. The Akaike and Schwartz tests were not as efficient as the other tests with few (8β10) data points. The I~p~ and the F test raise the percentages of right identification of the model when the hybrid elimination rate macroconstants differ by at least a factor of four. These large simulations show that the new selection criterion for pharmacokinetic multiexponential equations compares well with the F test and is superior to the Akaike and Schwartz tests.