RPEM: Randomized Monte Carlo Parametric Expectation Maximization Algorithm

TL;DR

The paper introduces RPEM, a novel Monte Carlo EM algorithm inspired by quantum Monte Carlo, which is faster and more accurate than existing methods for complex pharmacokinetic models.

Contribution

RPEM is a new high-performance Monte Carlo EM algorithm that uses unbiased estimators and simultaneous sampling of variables, outperforming existing methods in speed and accuracy.

Findings

RPEM is 3 to 4 times faster than SAEM and QRPEM.

RPEM provides more accurate parameter estimates.

Demonstrated on a complex two-compartment Voriconazole model.

Abstract

Inspired from quantum Monte Carlo, by using unbiased estimators all the time and sampling discrete and continuous variables at the same time using Metropolis 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). In particular, we compared RPEM with Monolix's SAEM and Certara's QRPEM for a realistic two-compartment Voriconazole model with ordinary differential equations (ODEs) and using simulated data. We show that RPEM is 3 to 4 times faster than SAEM and QRPEM, and more accurate than them in reconstructing the population parameters.