Stability and performance of a population pharmacokinetic model
Stability and performance of a population pharmacokinetic model
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DOI：
10.1002/j.1552-4604.1997.tb04326.x
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发表时间：
1997-06-01
影响因子：
2.9
通讯作者：
Ette, EI
中科院分区：
文献类型：
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作者：
Ette, EI
本研究旨在确定群体药代动力学模型的稳定性（协变量选择项），并在缺乏检验数据集的情况下评价其性能。使用探索性数据分析方法和非线性混合效应模型（NONMEM）程序分析了88例足月婴儿（其中21例为人类免疫缺陷病毒（HIV）血清阳性）服用抗感染药物的数据，以获得最终的群体药代动力学模型。采用非参数bootstrap方法分4步检验群体药代动力学模型的稳定性：1）使用基础药代动力学模型，通过替换抽样生成原始数据的100个bootstrap重复; 2）确定每个bootstrap日期！通过使用NONMEM目标函数的基本结构模型描述重复; 3）在α = 0.05和频率（f）截止值为0.50时，将广义加性建模（GAM）应用于经验贝叶斯估计以进行协变量选择;和4）使用在第三步中选择的协变量（α = 0.005）构建NONMEM群体模型。使用200个额外的bootstrap重复数据，通过拟合步骤4中获得的模型，评价群体药代动力学模型的性能。将获得的参数与模型稳定性步骤中获得的参数进行比较，并计算改进的预测误差（作为内部验证指标的预测准确度的测量）。GAM选择血清肌酐（RSC; f = 0.73）和HIV（f = 0.70）的倒数作为清除率（Cl）的预测因子。在未确定模型稳定性的情况下获得的群体药代动力学模型包括RSC作为Cl的预测因子，但模型稳定性步骤的最终模型包括HN和RSC作为Cl的预测因子。最终群体药代动力学参数是使用该模型拟合原始数据获得的;然而，HIV状态回归系数的95%置信区间包括零，表明无显著性。在回归稳定性步骤，使用额外200份自助重复数据获得的平均参数估计值在最终模型获得的平均参数估计值的25%范围内。Bootstrap回归方法是一种在没有检验数据集的情况下，通过对Bootstrap样本进行反复拟合来评价总体模型的稳定性和性能的方法。
This study aimed to determine the stability fin terms of covariate selection) of a population pharmacokinetic model and evaluate its performance in the absence of a test data set. Data from 88 full-term infants, 21 of whom were human immunodeficiency virus (HIV)-seropositive, taking an antiinfective agent were analyzed using exploratory data analysis methods and the nonlinear mixed-effects modeling (NONMEM) program to obtain the final population pharmacokinetic model. The stability of the population pharmacokinetic model was tested using the nonparametric bootstrap approach in four steps: 1) with the base pharmacokinetic model, 100 bootstrap replicates of the original data were generated by sampling with replacement; 2) ascertainment that each bootstrap date! replicate was described by the basic structural model using the NONMEM objective function; 3) generalized additive modeling (GAM) applied to empiric Bayesian estimates for covariate selection at alpha = 0.05 and a frequency (f) cutoff value of 0.50; and 4) NONMEM population model building using covariates selected in the third step with alpha = 0.005. Performance of the population pharmacokinetic model was evaluated using 200 additional bootstrap replicates of the data by fitting the model obtained in step 4 to them. Parameters obtained were compared with those obtained in the model stability step, and improved prediction error, a measure of predictive accuracy as an index of internal validation, was computed. The reciprocal of serum creatinine (RSC; f = 0.73) and HIV (f = 0.70) were selected by GAM as predictors of clearance (Cl). The population pharmacokinetic model obtained without the determination of model stability included RSC as a predictor of Cl, but the final model from the model stability step included both HN and RSC as predictors of Cl. Final population pharmacokinetic parameters were obtained with this model fitted to the original data; however, the 95% confidence interval on the HIV status regression coefficient included zero, indicating no significance. The mean parameter estimates obtained with the additional 200 bootstrap replicates of data were within 25% of those obtained with the final model at the regression stability step. Bootstrap resampling procedure is useful for evaluating the stability and performance of a population model by repeatedly fitting it to the bootstrap samples when there is no test data set.