Evidence map›Paper›PMID 38622792›Full record

ArticleCPT: pharmacometrics & systems pharmacology2024

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. RPEM: Randomized Monte Carlo parametric expectation maximization algorithm.CPT: pharmacometrics & systems pharmacology · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Rong ChenCertara, Inc., Princeton, New Jersey, USA.ORCID 0000-0001-8717-094X
Alan SchumitzkyLaboratory of Applied Pharmacokinetics and Bioinformatics, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Alona KryshchenkoDepartment of Mathematics, California State University Channel Islands, Camarillo, California, USA.ORCID 0000-0002-5049-0293
Keith NieforthCertara, Inc., Princeton, New Jersey, USA.
Michael TomashevskiyCertara, Inc., Princeton, New Jersey, USA.
Shuhua HuCertara, Inc., Princeton, New Jersey, USA.ORCID 0000-0002-4714-7478
Romain GarreauUMR CNRS 5558, Laboratoire de Biométrie et Biologie Evolutive, Université de Lyon, Université Lyon 1, Villeurbanne, France.ORCID 0000-0002-6605-4808
Julian OtalvaroLaboratory of Applied Pharmacokinetics and Bioinformatics, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.ORCID 0000-0001-5202-1645
Walter YamadaLaboratory of Applied Pharmacokinetics and Bioinformatics, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.ORCID 0000-0003-3512-9202
Michael N NeelyLaboratory of Applied Pharmacokinetics and Bioinformatics, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.ORCID 0000-0002-1675-8276

Funding

Optimizing Multi-drug Mycobacterium tuberculosis Therapy for Rapid Sterilization and Resistance SuppressionR01AI173238 · NIAID · UNIVERSITY OF FLORIDA · PI ARNOLD LOUIE, Michael N. Neely · 2023 to 2026
$5.4M
PBPK and Population Modeling Seamlessly Linked to Clinical Trial Simulation in an Open-Source Software PlatformU01FD006549 · FDA · CHILDREN'S HOSPITAL OF LOS ANGELES · PI EDGINTON, ANDREA, LIPPERT, JOERG · 2018 to 2020
$896k
FDA HHS U01 FD006549NIAID NIH HHS R01 AI173238
6 · The paper itself

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.

Indexed as

AlgorithmsMonte Carlo MethodVoriconazoleAntifungal AgentsComputer SimulationHumansAntifungal AgentsVoriconazole

Identifiers

PMID38622792
PMCPMC11098164

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.