Impact of omission or replacement of data below the limit of quantification on parameter estimates in a two-compartment model
Impact of omission or replacement of data below the limit of quantification on parameter estimates in a two-compartment model
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DOI：
10.1023/a:1021441407898
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发表时间：
2002-12-01
影响因子：
3.7
通讯作者：
Karlsson, MO
中科院分区：
文献类型：
--
作者：
Duval, V;Karlsson, MO
目的。评估使用非线性混合效应模型时，低于定量限 (LOQ) 的数据的省略和替换方法对两室模型药代动力学参数估计的影响。方法。根据具有个体间和残差变异的两室静脉推注模型模拟九个数据集，并采用稀疏采样策略。分布阶段和消除阶段之间的数据集在曲线下面积 (AUC) 比率（0.1、0.2、0.3）和半衰期比率（0.03、0.1、0.3）方面有所不同。对于九个数据集中的每一个，通过省略 5%、10%、20%、30%、40% 或 50% 的最低浓度值来创建六个简化数据集。对于每个缩减的数据集，仅应用一个简单的校正程序来处理低于 LOQ 的观察结果。 LOQ 以下的所有值均被删除，第一个值被 LOQ 值的一半替换。对 117 个结果数据集中的每一个进行总体参数估计（九个案例中的每一个都有一个初始数据集、六个简化数据集和六个“校正”数据集）。该方法还应用于接受多次静脉推注剂量的患者的真实数据集。结果。对于许多数据集，特别是当省略大部分数据时，一个或多个总体参数存在偏差。当存在偏差时，清除率（CL）通常被低估，而外周体积则被高估。与分布相相关的参数（中心体积和房间隙）受影响较小，并且变化不是系统性的。校正过程显着降低了参数固定效应的总体偏差。真实数据的结果相似。结论。遗漏低于 LOQ 值的数据可能会导致固定效应参数估计产生不可忽略的偏差。遗漏低于 LOQ 的值的影响与浓度-时间曲线的基本形状和遗漏观测值的比例有关。使用简单的替换规则似乎可以减少估计中的这种偏差，但需要进一步研究。
Purpose. To evaluate the influence of omission and replacement approaches for data below the limit of quantification (LOQ) on the estimation of pharmacokinetic parameters for two- compartment models when using nonlinear mixed- effect models.Method. Nine data sets were simulated according to a two-compartment intravenous bolus model with interindividual and residual variabilities, and a sparse sampling strategy was adopted. The data sets differed with respect to area- under- the- curve (AUC) ratio (0.1, 0.2, 0.3) and half- life ratio (0.03, 0.1, 0.3) between the distribution and elimination phases. For each of the nine data sets, six reduced data sets were created by omitting 5%, 10%, 20%, 30%, 40%, or 50% of the lowest concentration values. For each of the reduced data sets only one simple correction procedure to handle observations below LOQ was applied. All the values below the LOQ were deleted, and the first one was replaced by half of the LOQ value. Population parameters were estimated for each of the 117 resulting data sets (one initial, six reduced, and six "corrected" data sets for each of the nine cases). This approach was also applied on a real data set of patients administered multiple IV bolus doses.Results. For many of the data sets, particularly when a large fraction of the data was omitted, one or several population parameters were biased. When there was bias, clearance (CL) usually was underestimated, whereas peripheral volume was overestimated. The parameters related to the distribution phase (central volume and intercompartmental clearance) were less affected, and changes were not systematic. The correction procedure markedly decreased overall bias on the fixed effect of the parameters. Results for the real data were similar.Conclusion. Omission of data below the LOQ value may induce a not negligible bias on fixed- effect parameter estimates. The influence of the omission of values below LOQ was related to the underlying shape of the concentration- time profile and fraction of omitted observations. The use of a simple replacement rule seems to reduce this bias in estimates but needs further investigation.