Evaluation of the estimation and classification performance of NONMEM when applying mixture model for drug clearance.

ABSTRACT: Maximum likelihood estimation of parameters involving mixture model is known to have significant and specific patterns of errors. Population pharmacokinetic (PopPK) modeling using NONMEM is no exception. A few relevant studies on estimation and classification performance were done, but a comprehensive study was not yet available. The current study aims to evaluate performance and likelihood ratio test (LRT)-based true covariate detection rate when fitting a bimodal mixture of drug clearance (CL) in NONMEM. A large number of PopPK datasets with various settings were simulated and then estimated. The estimates were compared to the simulated values and summarized. The separation between the CL distributions of the two subpopulations is systematically overestimated. The major factor associated

SUBMITTER: Hui KH 

PROVIDER: S-EPMC8674007 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature