A sequential Monte Carlo approach to derive sampling times and windows for population pharmacokinetic studies
A sequential Monte Carlo approach to derive sampling times and windows for population pharmacokinetic studies
复制标题
用于导出群体药代动力学研究的采样时间和窗口的连续蒙特卡罗方法
DOI：
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发表时间：
2012
影响因子：
2.5
通讯作者：
A. Pettitt
中科院分区：
文献类型：
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作者：
James McGree;C. Drovandi;A. Pettitt
在这里，我们提出了一个顺序蒙特卡罗方法，可用于找到最佳设计。我们的重点是群体药代动力学研究的设计，其中采样窗口的推导是必需的，沿着最佳采样时间表。通过粒子滤波器进行搜索，该粒子滤波器遍历通过退火实用程序人工构建的目标分布序列。该算法推导出一个目录的高效设计，不仅包含最佳的，但也可以用来获得采样窗口。我们通过设计一个假设的群体药代动力学研究来证明我们的方法，并将我们的结果与文献中通过模拟方法获得的结果进行比较。
Here we present a sequential Monte Carlo approach that can be used to find optimal designs. Our focus is on the design of population pharmacokinetic studies where the derivation of sampling windows is required, along with the optimal sampling schedule. The search is conducted via a particle filter which traverses a sequence of target distributions artificially constructed via an annealed utility. The algorithm derives a catalog of highly efficient designs which, not only contain the optimal, but can also be used to derive sampling windows. We demonstrate our approach by designing a hypothetical population pharmacokinetic study, and compare our results with those obtained via a simulation method from the literature.