Evaluation of the Nonparametric Estimation Method in NONMEM VI: Application to Real Data

The aim of the study was to evaluate the nonparametric estimation methods available in NONMEM VI in comparison with the parametric first-order method (FO) and the first-order conditional estimation method (FOCE) when applied to real datasets. Four methods for estimating model parameters and parameter distributions (FO, FOCE, nonparametric preceded by FO (FO-NONP) and nonparametric preceded by FOCE (FOCE-NONP)) were compared for 25 models previously developed using real data and a parametric method. Numerical predictive checks were used to test the appropriateness of each model. Up to 1000 new datasets were simulated from each model and with each method to construct 90% and 50% prediction intervals. The mean absolute error and the mean error of the different outcomes investigated were computed as indicators of imprecision and bias respectively and formal statistical tests were performed. Overall, less imprecision and less bias were observed with nonparametric methods than with parametric methods. Across the 25 models, t-tests revealed that imprecision and bias were significantly lower (P < 0.05) with FOCE-NONP than with FOCE for half of the NPC outcomes investigated. Improvements were even more pronounced with FO-NONP in comparison with FO. In conclusion, when applied to real datasets and evaluated by numerical predictive checks, the nonparametric estimation methods in NONMEM VI performed better than the corresponding parametric methods (FO or FOCE).