Incorporation of concentration data below the limit of quantification in population pharmacokinetic analyses.
Incorporation of concentration data below the limit of quantification in population pharmacokinetic analyses.
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DOI：
10.1002/prp2.131
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发表时间：
2015-03
影响因子：
2.6
通讯作者：
Huitema, Alwin D R
中科院分区：
文献类型：
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作者：
Keizer, Ron J;Jansen, Robert S;Rosing, Hilde;Thijssen, Bas;Beijnen, Jos H;Schellens, Jan H M;Huitema, Alwin D R
在群体药代动力学（PopPK）分析中，低于定量下限（LLOQ）和定量限（BLOQ）的数据处理对于减少参数估计的偏倚和不精确性非常重要。我们旨在评价使用低于LLOQ的浓度数据是否具有优于几种已建立方法的上级性能。评价了该方法的性能（“所有数据”），并与其他方法进行了比较：“丢弃”、“LLOQ/2”和“LIKE”（基于可能性）。基于内部分析方法验证和文献分析构建了分析和残差模型，并纳入了额外的变异性以解释模型错误规定。对不同水平的BLOQ、几种结构PopPK模型和其他影响进行了模拟分析。通过相对均方根误差（RMSE）和各种BLOQ方法的运行成功率来评估性能。还评价了真实的PopPK数据集的性能。对于所有PopPK模型和删失水平，使用“所有数据”的RMSE值最低。“LIKE”方法的性能优于“LLOQ/2”或“丢弃”方法。在最低水平的BLOQ删失时，所有方法之间的差异都很小。“LIKE”方法导致低成功的最小化（<50%）和协方差步骤成功（<30%），尽管在大多数运行中获得了估计值（> 90%）。对于真实的PK数据集（7.4% BLOQ），使用所有方法获得的参数估计值相似。与已建立的BLOQ方法相比，纳入BLOQ浓度在偏倚和精密度方面表现出上级性能，并在真实的PopPK分析中表现出可行性。
Handling of data below the lower limit of quantification (LLOQ), below the limit of quantification (BLOQ) in population pharmacokinetic (PopPK) analyses is important for reducing bias and imprecision in parameter estimation. We aimed to evaluate whether using the concentration data below the LLOQ has superior performance over several established methods. The performance of this approach (“All data”) was evaluated and compared to other methods: “Discard,” “LLOQ/2,” and “LIKE” (likelihood-based). An analytical and residual error model was constructed on the basis of in-house analytical method validations and analyses from literature, with additional included variability to account for model misspecification. Simulation analyses were performed for various levels of BLOQ, several structural PopPK models, and additional influences. Performance was evaluated by relative root mean squared error (RMSE), and run success for the various BLOQ approaches. Performance was also evaluated for a real PopPK data set. For all PopPK models and levels of censoring, RMSE values were lowest using “All data.” Performance of the “LIKE” method was better than the “LLOQ/2” or “Discard” method. Differences between all methods were small at the lowest level of BLOQ censoring. “LIKE” method resulted in low successful minimization (<50%) and covariance step success (<30%), although estimates were obtained in most runs (∼90%). For the real PK data set (7.4% BLOQ), similar parameter estimates were obtained using all methods. Incorporation of BLOQ concentrations showed superior performance in terms of bias and precision over established BLOQ methods, and shown to be feasible in a real PopPK analysis.