Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, 4001, Australia.
J Pharmacokinet Pharmacodyn. 2012 Oct;39(5):519-26. doi: 10.1007/s10928-012-9265-1. Epub 2012 Jul 31.
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.
在这里，我们提出了一种序贯蒙特卡罗方法，可用于寻找最佳设计。我们的重点是设计群体药代动力学研究，其中需要推导采样窗口以及最佳采样计划。搜索是通过粒子滤波器进行的，该滤波器通过通过退火效用人工构建的一系列目标分布进行遍历。该算法生成了一个高效设计目录，不仅包含最优设计，还可以用于推导采样窗口。我们通过设计一个假设的群体药代动力学研究来演示我们的方法，并将我们的结果与文献中模拟方法获得的结果进行比较。
