Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resampling.
Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resampling.
复制标题
使用采样重要性重新采样，改善非线性混合效应模型中参数不确定性分布的估计。
DOI：
10.1007/s10928-016-9487-8
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发表时间：
2016-12
影响因子：
2.5
通讯作者：
Karlsson, Mats O.
中科院分区：
文献类型：
--
作者：
Dosne, Anne-Gaelle;Bergstrand, Martin;Harling, Kajsa;Karlsson, Mats O.
关键词：
考虑参数不确定性是做出药物开发决策的关键，例如测试试验终点是否符合定义的标准。目前用于评估 NLMEM 中参数不确定性的方法存在局限性，并且缺乏对这些局限性何时发生的诊断。在这项工作中，提出了一种基于采样重要性重采样（SIR）的方法，该方法的优点是不受分布假设的影响，并且不需要重复的参数估计。为了执行 SIR，需要根据给定的建议不确定性分布来模拟大量参数向量。然后，在给定真实不确定性的情况下，它们的可能性通过给定每个向量的数据的可能性与给定建议分布的每个向量的可能性之间的比率来近似，称为重要性比。非参数不确定性分布是通过根据与其重要性比成比例的概率对参数向量进行重采样来获得的。使用两个仿真示例和三个实际数据示例来定义如何使用 NLMEM 执行 SIR 并研究该方法的性能。仿真实例表明SIR能够恢复真实的参数不确定性。真实数据示例表明，当 95% CI 对称时，使用 SIR 获得的参数 95% 置信区间 (CI)、协方差矩阵、自举和对数似然分析通常是一致的。对于显示不对称 95% CI 的参数，SIR 95% CI 与对数似然分析非常一致，但通常与 bootstrap 95% CI 不同，后者已被证明对于所选示例来说不是最佳的。这项工作还为 SIR 工作流程提供了指导，即使用为此目的开发的诊断程序，在执行 SIR 时选择哪种提案分布以及采样多少个参数向量。 SIR 是一种很有前途的评估参数不确定性的方法，因为它适用于许多其他评估参数不确定性的方法失败的情况，例如存在小数据集、高度非线性模型或荟萃分析的情况。本文的在线版本 (doi:10.1007/s10928-016-9487-8) 包含补充材料，可供授权用户使用。
Taking parameter uncertainty into account is key to make drug development decisions such as testing whether trial endpoints meet defined criteria. Currently used methods for assessing parameter uncertainty in NLMEM have limitations, and there is a lack of diagnostics for when these limitations occur. In this work, a method based on sampling importance resampling (SIR) is proposed, which has the advantage of being free of distributional assumptions and does not require repeated parameter estimation. To perform SIR, a high number of parameter vectors are simulated from a given proposal uncertainty distribution. Their likelihood given the true uncertainty is then approximated by the ratio between the likelihood of the data given each vector and the likelihood of each vector given the proposal distribution, called the importance ratio. Non-parametric uncertainty distributions are obtained by resampling parameter vectors according to probabilities proportional to their importance ratios. Two simulation examples and three real data examples were used to define how SIR should be performed with NLMEM and to investigate the performance of the method. The simulation examples showed that SIR was able to recover the true parameter uncertainty. The real data examples showed that parameter 95 % confidence intervals (CI) obtained with SIR, the covariance matrix, bootstrap and log-likelihood profiling were generally in agreement when 95 % CI were symmetric. For parameters showing asymmetric 95 % CI, SIR 95 % CI provided a close agreement with log-likelihood profiling but often differed from bootstrap 95 % CI which had been shown to be suboptimal for the chosen examples. This work also provides guidance towards the SIR workflow, i.e.,which proposal distribution to choose and how many parameter vectors to sample when performing SIR, using diagnostics developed for this purpose. SIR is a promising approach for assessing parameter uncertainty as it is applicable in many situations where other methods for assessing parameter uncertainty fail, such as in the presence of small datasets, highly nonlinear models or meta-analysis. The online version of this article (doi:10.1007/s10928-016-9487-8) contains supplementary material, which is available to authorized users.
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影响因子：
6.1
作者：
Lindbom, L;Pihlgren, P;Jonsson, N
通讯作者：
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DOI：
10.1159/000457062
发表时间：
1985-01-01
期刊：
DEVELOPMENTAL PHARMACOLOGY AND THERAPEUTICS
影响因子：
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DOI：
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发表时间：
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影响因子：
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作者：
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影响因子：
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