RPEM: Randomized Monte Carlo parametric expectation maximization algorithm - Rong Chen; Alan Schumitzky; Alona Kryshchenko; Keith Nieforth; Michael Tomashevskiy; Shuhua Hu; Romain Garreau; Julian Otalvaro; Walter Yamada; Michael N. Neely (Wiley, 2024)
Abstract 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.