...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of pharmacokinetics and pharmacodynamics >Comparing the performance of FOCE and different expectation-maximization methods in handling complex population physiologically-based pharmacokinetic models
【24h】

Comparing the performance of FOCE and different expectation-maximization methods in handling complex population physiologically-based pharmacokinetic models

机译：比较FORCE和不同的期望最大化方法在处理复杂人群基于生理学的药代动力学模型中的性能

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

For the purpose of population pharmacometric modeling, a variety of mathematic algorithms are implemented in major modeling software packages to facilitate the maximum likelihood modeling, such as FO, FOCE, Laplace, ITS and EM. These methods are all designed to estimate the set of parameters that maximize the joint likelihood of observations in a given problem. While FOCE is still currently the most widely used method in population modeling, EM methods are getting more popular as the current-generation methods of choice because of their robustness with more complex models and sparse data structures. There are several versions of EM method implementation that are available in public modeling software packages. Although there have been several studies and reviews comparing the performance of different methods in handling relatively simple models, there has not been a dedicated study to compare different versions of EM algorithms in solving complex PBPK models. This study took everolimus as a model drug and simulated PK data based on published results. Three most popular EM methods (SAEM, IMP and QRPEM) and FOCE (as a benchmark reference) were evaluated for their estimation accuracy and converging speed when solving models of increased complexity. Both sparse and rich sampling data structure were tested. We concluded that FOCE was superior to EM methods for simple structured models. For more complex models and/ or sparse data, EM methods are much more robust. While the estimation accuracy was very close across EM methods, the general ranking of speed (fastest to slowest) was: QRPEM, IMP and SAEM. IMP gave the most realistic estimation of parameter standard errors, while under- and over- estimation of standard errors were observed in SAEM and QRPEM methods.
机译：为了进行群体药理学建模，主要的建模软件包中采用了多种数学算法来促进最大似然建模，例如FO，FOCE，Laplace，ITS和EM。所有这些方法都是为了估计一组参数，以使给定问题中的观测值的联合可能性最大。尽管FOCE仍然是当前在人口模型中使用最广泛的方法，但由于EM方法具有更复杂的模型和稀疏的数据结构，因此它们作为当前选择的方法越来越受欢迎。公共建模软件包中提供了EM方法实现的多个版本。尽管已经有一些研究和评论比较了不同方法在处理相对简单的模型中的性能，但是还没有专门的研究来比较EM算法的不同版本来解决复杂的PBPK模型。这项研究以依维莫司为模型药物，并根据已发表的结果模拟了PK数据。在解决复杂性增加的模型时，对三种最流行的EM方法（SAEM，IMP和QRPEM）和FOCE（作为基准参考）进行了评估，评估了它们的估计准确性和收敛速度。测试了稀疏和丰富的采样数据结构。我们得出的结论是，对于简单的结构化模型，FOCE优于EM方法。对于更复杂的模型和/或稀疏数据，EM方法更为健壮。尽管在所有EM方法中估计精度都非常接近，但速度的总体排名（从最快到最慢）是：QRPEM，IMP和SAEM。 IMP提供了最真实的参数标准误差估计，而SAEM和QRPEM方法则发现标准误差过低和过高。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号