Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

NONMEM Tutorial Part I: Description of Commands and Options, with Simple Examples of Population Analysis.

ABSTRACT: In this tutorial, the various components of NONMEM^® will be described, and basic steps of setting up NONMEM control stream files and data files will be demonstrated. Some basic concepts of nonlinear mixed effects modelling will be discussed, along with simple examples demonstrating how to use NONMEM to perform population analysis of clinical data. This article is protected by copyright. All rights reserved.

SUBMITTER: Bauer RJ

PROVIDER: S-EPMC6709426 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

NONMEM Tutorial Part I: Description of Commands and Options, With Simple Examples of Population Analysis.

Bauer Robert J RJ

CPT: pharmacometrics & systems pharmacology 20190613 8

In this tutorial, the various components of NONMEM will be described, and the basic steps of setting up NONMEM control stream files and data files will be demonstrated. Some basic concepts of nonlinear mixed effects modeling will be discussed, along with simple examples demonstrating how to use NONMEM to perform population analysis of clinical data. ...[more]

PMID: 31056834

Similar Datasets

NONMEM Tutorial Part II: Estimation Methods and Advanced Examples.

Project description:In this second tutorial on NONMEM, examples of typical PK/PD modeling problems that occur in the pharmaceutical field will be presented, and which the reader can utilize as a template for his own modeling endeavors. Each of the problems presented are challenging in some way, and the logic behind setting up each problem is discussed. Logical concepts of the problem itself as well as the technical aspect of how to set it up in NONMEM are described and demonstrated. The concepts behind the various estimation algorithms will first be described to allow the user a better understanding of how to use them. This article is protected by copyright. All rights reserved.

| S-EPMC6709422 | biostudies-other

Tutorial for $DESIGN in NONMEM: Clinical trial evaluation and optimization.

Project description:This NONMEM tutorial shows how to evaluate and optimize clinical trial designs, using algorithms developed in design software, such as PopED and PFIM 4.0. Parameter precision and model parameter estimability is obtained by assessing the Fisher Information Matrix (FIM), providing expected model parameter uncertainty. Model parameter identifiability may be uncovered by very large standard errors or inability to invert an FIM. Because evaluation of FIM is more efficient than clinical trial simulation, more designs can be investigated, and the design of a clinical trial can be optimized. This tutorial provides simple and complex pharmacokinetic/pharmacodynamic examples on obtaining optimal sample times, doses, or best division of subjects among design groups. Robust design techniques accounting for likely variability among subjects are also shown. A design evaluator and optimizer within NONMEM allows any control stream first developed for trial design exploration to be subsequently used for estimation of parameters of simulated or clinical data, without transferring the model to another software. Conversely, a model developed in NONMEM could be used for design optimization. In addition, the $DESIGN feature can be used on any model file and dataset combination to retrospectively evaluate the model parameter uncertainty one would expect given that the model generated the data, particularly if outliers of the actual data prevent a reasonable assessment of the variance-covariance. The NONMEM trial design feature is suitable for standard continuous data, whereas more elaborate trial designs or with noncontinuous data-types can still be accomplished in optimal design dedicated software like PopED and PFIM.

| S-EPMC8674001 | biostudies-literature

Modeling and Simulation Workbench for NONMEM: Tutorial on Pirana, PsN, and Xpose.

Project description:Several software tools are available that facilitate the use of the NONMEM software and extend its functionality. This tutorial shows how three commonly used and freely available tools, Pirana, PsN, and Xpose, form a tightly integrated workbench for modeling and simulation with NONMEM. During the tutorial, we provide some guidance on what diagnostics we consider most useful in pharmacokinetic model development and how to construct them using these tools.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e50; doi:10.1038/psp.2013.24; advance online publication 26 June 2013.

| S-EPMC3697037 | biostudies-literature

Transformation of stimulus value signals into motor commands during simple choice.

Project description:Decision-making can be broken down into several component processes: assigning values to stimuli under consideration, selecting an option by comparing those values, and initiating motor responses to obtain the reward. Although much is known about the neural encoding of stimulus values and motor commands, little is known about the mechanisms through which stimulus values are compared, and the resulting decision is transmitted to motor systems. We investigated this process using human fMRI in a task where choices were indicated using the left or right hand. We found evidence consistent with the hypothesis that value signals are computed in the ventral medial prefrontal cortex, they are passed to regions of dorsomedial prefrontal cortex and intraparietal sulcus, implementing a comparison process, and the output of the comparator regions modulates activity in motor cortex to implement the choice. These results describe the network through which stimulus values are transformed into actions during a simple choice task.

| S-EPMC3207676 | biostudies-literature

LaTeX tutorial for the standardization and automation of population analysis reports.

Project description:Writing population analysis reports that fulfilling the specifications of submission readiness is a time-consuming process and prone to human error. Thus, there is a need to streamline the creation of these reports through elements of standardization and automation. LaTeX is considered a suitable program package capable of creating long, modular, structured documents, and, because of its typographic quality, includes formula typesetting. The presented automation scripts together with easily adjustable LaTeX templates are designed to enable the reader to understand and reproduce a typical workflow from analysis to reporting. The focus of this tutorial is to use an example of a population pharmacokinetic analysis to show how to work with the proposed automated structures allowing even a reader new to the concept of LaTeX to automatize the reporting workflow and customize the templates for their specific needs.

| S-EPMC8592516 | biostudies-literature

Erratum: R-based reproduction of the estimation process hidden behind NONMEM Part 2: First-order conditional estimation.

Project description:[This corrects the article on p. 161 in vol. 24.].

| S-EPMC6989257 | biostudies-literature

Motor Cortex Embeds Muscle-like Commands in an Untangled Population Response.

Project description:Primate motor cortex projects to spinal interneurons and motoneurons, suggesting that motor cortex activity may be dominated by muscle-like commands. Observations during reaching lend support to this view, but evidence remains ambiguous and much debated. To provide a different perspective, we employed a novel behavioral paradigm that facilitates comparison between time-evolving neural and muscle activity. We found that single motor cortex neurons displayed many muscle-like properties, but the structure of population activity was not muscle-like. Unlike muscle activity, neural activity was structured to avoid "tangling": moments where similar activity patterns led to dissimilar future patterns. Avoidance of tangling was present across tasks and species. Network models revealed a potential reason for this consistent feature: low tangling confers noise robustness. Finally, we were able to predict motor cortex activity from muscle activity by leveraging the hypothesis that muscle-like commands are embedded in additional structure that yields low tangling.

| S-EPMC5823788 | biostudies-literature

Prior information for population pharmacokinetic and pharmacokinetic/pharmacodynamic analysis: overview and guidance with a focus on the NONMEM PRIOR subroutine.

Project description:Population pharmacokinetic analysis is used to estimate pharmacokinetic parameters and their variability from concentration data. Due to data sparseness issues, available datasets often do not allow the estimation of all parameters of the suitable model. The PRIOR subroutine in NONMEM supports the estimation of some or all parameters with values from previous models, as an alternative to fixing them or adding data to the dataset. From a literature review, the best practices were compiled to provide a practical guidance for the use of the PRIOR subroutine in NONMEM. Thirty-three articles reported the use of the PRIOR subroutine in NONMEM, mostly in special populations. This approach allowed fast, stable and satisfying modelling. The guidance provides general advice on how to select the most appropriate reference model when there are several previous models available, and to implement and weight the selected parameter values in the PRIOR function. On the model built with PRIOR, the similarity of estimates with the ones of the reference model and the sensitivity of the model to the PRIOR values should be checked. Covariates could be implemented a priori (from the reference model) or a posteriori, only on parameters estimated without prior (search for new covariates).

| S-EPMC7520416 | biostudies-literature

Bayesian Latent Class Analysis Tutorial.

Project description:This article is a how-to guide on Bayesian computation using Gibbs sampling, demonstrated in the context of Latent Class Analysis (LCA). It is written for students in quantitative psychology or related fields who have a working knowledge of Bayes Theorem and conditional probability and have experience in writing computer programs in the statistical language R . The overall goals are to provide an accessible and self-contained tutorial, along with a practical computation tool. We begin with how Bayesian computation is typically described in academic articles. Technical difficulties are addressed by a hypothetical, worked-out example. We show how Bayesian computation can be broken down into a series of simpler calculations, which can then be assembled together to complete a computationally more complex model. The details are described much more explicitly than what is typically available in elementary introductions to Bayesian modeling so that readers are not overwhelmed by the mathematics. Moreover, the provided computer program shows how Bayesian LCA can be implemented with relative ease. The computer program is then applied in a large, real-world data set and explained line-by-line. We outline the general steps in how to extend these considerations to other methodological applications. We conclude with suggestions for further readings.

| S-EPMC6364555 | biostudies-literature

Simple, fast, and accurate methodology for quantitative analysis using Fourier transform infrared spectroscopy, with bio-hybrid fuel cell examples.

Project description:The standard methodologies for quantitative analysis (QA) of mixtures using Fourier transform infrared (FTIR) instruments have evolved until they are now more complicated than necessary for many users' purposes. We present a simpler methodology, suitable for widespread adoption of FTIR QA as a standard laboratory technique across disciplines by occasional users.•Algorithm is straightforward and intuitive, yet it is also fast, accurate, and robust.•Relies on component spectra, minimization of errors, and local adaptive mesh refinement.•Tested successfully on real mixtures of up to nine components. We show that our methodology is robust to challenging experimental conditions such as similar substances, component percentages differing by three orders of magnitude, and imperfect (noisy) spectra. As examples, we analyze biological, chemical, and physical aspects of bio-hybrid fuel cells.

| S-EPMC4781924 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data