Evaluation of the nonparametric estimation method in NONMEM VI

Abstract

Purpose In NONMEM VI, a novel method exists for estimation of a nonparametric parameter distribution. The parameter distributions are approximated by discrete probability density functions at a number of parameter values (support points). The support points are obtained from the empirical Bayes estimates from a preceding parametric estimation step, run with the First Order (FO) or First Order Conditional Estimation (FOCE) methods. The purpose of this work is to evaluate this new method with respect to parameter distribution estimation. Methods The performance of the method, with special emphasis on the analysis of data with non-normal distribution of random effects, was studied using Monte Carlo (MC) simulations. Results The mean value (and ranges) of absolute relative biases (ARBs, %) in parameter distribution estimates with nonparametric methods preceded with FO and FOCE were 0.80 (0.1–3.7) and 0.70 (0–3), respectively, while for parametric methods, these values were 23.74 (3.3–97.5) and 4.38 (0.1–17.9), for FO and FOCE, respectively. The nonparametric estimation method in NONMEM could identify non-normal parameter distributions and correct bias in parameter estimates seen when applying the FO estimation method. Conclusions The method shows promising properties when analyzing different types of pharmacokinetic (PK) data with both the FO and FOCE methods as preceding steps.