Yoon SeokKyu, Lim Hyeong-Seok
Department of Clinical Pharmacology and Therapeutics, Asan Medical Center, University of Ulsan, Seoul 05505, Republic of Korea.
Transl Clin Pharmacol. 2019 Dec;27(4):141-148. doi: 10.12793/tcp.2019.27.4.141. Epub 2019 Dec 31.
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.
NONMEM®中混合模型的准确性和可预测性可能会因个体间差异的相对大小以及亚组之间参数差异的大小而有所变化。本研究探讨了混合模型在处理现实中可能出现的各种情况下缺失分类协变量时的准确性。我们在不同场景下生成了模拟数据，其中代表药物代谢酶的广泛代谢者（EM）和慢代谢者（PM）的基因型对药物清除率有不同程度的影响，并且每个场景下清除率的个体间变异也不同。从每个模拟数据中，随机去除特定比例的协变量（基因型信息）。基于这些模拟数据，使用混合模型估计每个个体亚组的比例和清除率。总体而言，当亚组之间的清除率差异较大且个体间变异较小时，清除率估计更准确。在一些显示出较高的ETA或epsilon收缩的场景中，清除率估计存在显著偏差。混合模型对EM亚组个体的预测比对PM亚组个体的预测更好。然而，如果样本量在一定程度上得到保证，估计值不受测试比例的显著影响。当前的模拟研究表明，当清除率个体间变异的变异系数超过40%时，应谨慎使用混合模型，并且应考虑到收缩可能会使结果产生偏差。
