RPEM: Randomized Monte Carlo parametric expectation maximization algorithm

AbstractInspired 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 A pproximation 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.

Source: CPT: Pharmacometrics and Systems Pharmacology - April 16, 2024 Category: Drugs & Pharmacology Authors: Rong Chen, Alan Schumitzky, Alona Kryshchenko, Keith Nieforth, Michael Tomashevskiy, Shuhua Hu, Romain Garreau, Julian Otalvaro, Walter Yamada, Michael N. Neely Tags: ARTICLE Source Type: research