Certara, Inc., Princeton, New Jersey, USA.
Laboratory of Applied Pharmacokinetics and Bioinformatics, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
CPT Pharmacometrics Syst Pharmacol. 2024 May;13(5):759-780. doi: 10.1002/psp4.13113. Epub 2024 Apr 15.
Inspired from quantum Monte Carlo, by sampling discrete and continuous variables at the same time using the Metropolis-Hastings algorithm, we present a novel, fast, and accurate high performance Monte Carlo Parametric Expectation Maximization (MCPEM) algorithm. We named it Randomized Parametric Expectation Maximization (RPEM). We compared RPEM with NONMEM's Importance Sampling Method (IMP), Monolix's Stochastic Approximation Expectation Maximization (SAEM), and Certara's Quasi-Random Parametric Expectation Maximization (QRPEM) for a realistic two-compartment voriconazole model with ordinary differential equations using simulated data. We show that RPEM is as fast and as accurate as the algorithms IMP, QRPEM, and SAEM for the voriconazole model in reconstructing the population parameters, for the normal and log-normal cases.
受量子蒙特卡罗启发，我们同时使用 Metropolis-Hastings 算法对离散和连续变量进行采样，提出了一种新颖、快速、准确的高性能蒙特卡罗参数期望最大化 (MCPEM) 算法。我们将其命名为随机参数期望最大化 (RPEM)。我们使用模拟数据，针对具有常微分方程的现实两室伏立康唑模型，将 RPEM 与 NONMEM 的重要性采样法 (IMP)、Monolix 的随机逼近期望最大化 (SAEM) 和 Certara 的拟随机参数期望最大化 (QRPEM) 进行了比较。我们证明，对于伏立康唑模型，在重建群体参数方面，RPEM 与 IMP、QRPEM 和 SAEM 一样快速和准确，无论是在正态分布还是对数正态分布情况下都是如此。
