논문/저서
| Performance of a mixture model by the degree of a missing categorical covariate when estimating clearance in NONMEM. | ||
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Transl Clin Pharmacol. 2019 Dec;27(4):141-148. doi: 10.12793/tcp.2019.27.4.141. Epub 2019 Dec 31. Performance of a mixture model by the degree of a missing categorical covariate when estimating clearance in NONMEM. Yoon S, Lim HS
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 data, the proportion of each individual subpopulation and the clearance were estimated using a mixture model. Overall, the clearance estimate was more accurate when the difference in clearance between subpopulations was large, and the inter-individual variations were small. In some scenarios that showed higher ETA or epsilon shrinkage, the clearance estimates were significantly biased. The mixture model made better predictions for individuals in the EM subpopulation than for individuals in the PM subpopulation. However, the estimated values were not significantly affected by the tested ratio, if the sample size was secured to some extent. The current simulation study suggests that when the coefficient of variation of inter-individual variations of clearance exceeds 40%, the mixture model should be used carefully, and it should be taken into account that shrinkage can bias the results.
KEYWORDS: Covariate; Missing categorical; Mixture model; NONMEM
PMID: 32095482 PMCID: DOI:
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