Performance of a mixture model by the degree of a missing categorical covariate when estimating clearance in NONMEM.

ABSTRACT: The accuracy and predictability of mixture models in NONMEM® may change depending on the relative size of inter-individual differences and the size of the differences in the parameters between subpopulations. This study explored the accuracy of mixture models when dealing with missing a categorical covariate under various situations that may occur in reality. We generated simulation data under various scenarios where genotypes representing extensive metabolizers (EM) and poor metabolizers (PM) of drug-metabolizing enzymes affect the clearance of a drug by different degrees, and the inter-individual variations in clearance are different for each scenario. From each simulated datum, a specific proportion of the covariate (genotype information) was randomly removed. Based on these simulation

SUBMITTER: Yoon S 

PROVIDER: S-EPMC7032962 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature