Evaluation of bootstrap methods for estimating uncertainty of parameters in nonlinear mixed-effects models: a simulation study in population pharmacokinetics
Evaluation of bootstrap methods for estimating uncertainty of parameters in nonlinear mixed-effects models: a simulation study in population pharmacokinetics
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DOI：
10.1007/s10928-013-9343-z
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发表时间：
2014-02-01
影响因子：
2.5
通讯作者：
Comets, Emmanuelle
中科院分区：
文献类型：
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作者：
Hoai-Thu Thai;Mentre, France;Comets, Emmanuelle
Bootstrap方法在许多学科中被用来估计参数的不确定性，包括多水平或线性混合效应模型。基于残差的Bootstrap方法同时对随机效应和残差进行重采样，是对个体进行重采样的情况Bootstrap的替代方法。大多数PKPD应用程序使用Case Bootstrap，其软件是可用的。本文通过模拟研究评价了三种Bootstrap方法(案例Bootstrap、非参数残差Bootstrap和参数Bootstrap)的性能，并将它们与渐近方法(ASYM)在具有异方差误差的非线性混合效应模型(NLMEM)中的参数不确定性估计进行了比较。这一模拟是以抗血管生成药物阿普利赛特的PK模型为例进行的。正如预期的那样，我们发现Bootstrap方法为高度非线性的NLMEM中的参数提供了更好的不确定性估计，并且与在MONOLIX中实现的ASYM相比，具有平衡的设计。总体而言，当使用真实模型和方差分布时，参数自举比案例自举表现得更好。然而，案例Bootstrap更快、更简单，因为它不对模型做任何假设，并且在一个重采样步骤中保留了对象和残差之间的变异性。在非平衡设计中，Asym和参数Bootstrap的表现很好，甚至在分层的情况下，非参数剩余Bootstrap的表现也很好，而且在应用于NLMEM时，由于它未能在重采样前重新计算方差，因此其性能有限。
Bootstrap methods are used in many disciplines to estimate the uncertainty of parameters, including multi-level or linear mixed-effects models. Residual-based bootstrap methods which resample both random effects and residuals are an alternative approach to case bootstrap, which resamples the individuals. Most PKPD applications use the case bootstrap, for which software is available. In this study, we evaluated the performance of three bootstrap methods (case bootstrap, nonparametric residual bootstrap and parametric bootstrap) by a simulation study and compared them to that of an asymptotic method (Asym) in estimating uncertainty of parameters in nonlinear mixed-effects models (NLMEM) with heteroscedastic error. This simulation was conducted using as an example of the PK model for aflibercept, an anti-angiogenic drug. As expected, we found that the bootstrap methods provided better estimates of uncertainty for parameters in NLMEM with high nonlinearity and having balanced designs compared to the Asym, as implemented in MONOLIX. Overall, the parametric bootstrap performed better than the case bootstrap as the true model and variance distribution were used. However, the case bootstrap is faster and simpler as it makes no assumptions on the model and preserves both between subject and residual variability in one resampling step. The performance of the nonparametric residual bootstrap was found to be limited when applying to NLMEM due to its failure to reflate the variance before resampling in unbalanced designs where the Asym and the parametric bootstrap performed well and better than case bootstrap even with stratification.