Model evaluation.

A bootstrap using Perl-speaks-NONMEM (PsN) with 1,000 samples was performed, and the parameters derived were summarized as median and 2.5th, and 97.5th percentiles (95% empirical confidence intervals [CI]) to facilitate evaluation of final model parameter estimates. In addition, prediction-corrected visual predictive checks (pcVPCs) were performed with 1,000 data sets simulated from the final models using PsN. The observed 10th, 50th, and 90th percentiles were plotted with their respective simulated 95% CIs. Numerical predictive checks (NPCs) were performed to complement the pcVPCs in assessing the predictive performance of the model. Given that both models had the same structure, it was possible to quantify these differences statistically using a chi-square distribution with 2 degrees of freedom to compare the difference between the resultant OFVs.