Pirana和集成PMX工具,一个用于NONMEM、NLME、pyDarwin和RsNLME的工作台。

IF 3.1 3区 医学 Q2 PHARMACOLOGY & PHARMACY
Rong Chen, Mark Sale, James Craig, Michael Tomashevskiy, Alex Mazur, Shuhua Hu, Keith Nieforth
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引用次数: 0

摘要

Keizer最初将Pirana描述为一个工作台,旨在简化NONMEM建模、可视化和分析的管理。最初版本的Pirana集成工具包括NONMEM和PSN。随着药物计量学家可以使用新的工具,Pirana也增加了新的功能。这些功能包括:通过R - speaks NLME (RsNLME)包集成NLME引擎,用于开发NLME模型;集成用于构建NLME模型的Shiny图形界面;集成机器学习pyDarwin python包;集成用于自定义诊断的Shiny界面,包括拟合度(GOF)图、表、视觉预测检查(VPC)和报告shell生成;我们提出了一个完整的工作流程,演示如何使用Pirana构建,适合,后处理,并使用NONMEM和NLME模型上执行VPC。此外,我们还展示了如何使用机器学习驱动的pyDarwin包与Pirana一起自动搜索模型结构、随机效应和协变量模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pirana and Integrated PMX Tools, a Workbench for NONMEM, NLME, pyDarwin, and RsNLME.

Keizer initially described Pirana as a workbench designed to streamline management of NONMEM modeling, visualization, and analysis. Initial versions of Pirana integrated tools included NONMEM and PSN. As new tools have become available to pharmacometricians, new capabilities have been added to Pirana. These capabilities include: Integration of the NLME engine via the R speaks NLME (RsNLME) package for developing NLME models Integration of a Shiny graphical interface for the construction of NLME models Integration of machine learning pyDarwin python package Integration of a Shiny interface for custom diagnostics including Goodness of Fit (GOF) plots, tables, Visual Predictive Check (VPC) and report shell generation Improved setup with support for parallel execution on a wide range of platforms In this tutorial, we present a full workflow demonstrating how to use Pirana to build, fit, post-process, and perform VPC on models using NONMEM and NLME. In addition, we show how to use the machine learning-driven pyDarwin package with Pirana to automatically search model structures, random effects, and covariate models.

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来源期刊
CiteScore
5.00
自引率
11.40%
发文量
146
审稿时长
8 weeks
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