一个简单的具有右审查功能的NONMEM时间到事件模型。

IF 1.1 Q4 PHARMACOLOGY & PHARMACY

Translational and Clinical Pharmacology Pub Date : 2022-06-01 Epub Date: 2022-06-15 DOI:10.12793/tcp.2022.30.e8

Quyen Thi Tran, Jung-Woo Chae, Kyun-Seop Bae, Hwi-Yeol Yun

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引用次数: 0

摘要

在医疗保健情况下，事件时间(TTE)数据是常见的结果。通常采用参数化方法来处理TTE数据，因为通过模拟可以很容易地将不同的场景可视化。并非所有的药物计量学家都熟悉使用非线性混合效应模型(NONMEMs)来处理TTE数据。因此，本教程只解释如何使用NONMEM分析TTE数据。我们将展示如何编写代码并评估模型。我们还提供了一个用于培训的动手模型示例。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A simple time-to-event model with NONMEM featuring right-censoring.

In healthcare situations, time-to-event (TTE) data are common outcomes. A parametric approach is often employed to handle TTE data because it is possible to easily visualize different scenarios via simulation. Not all pharmacometricians are familiar with the use of non-linear mixed effects models (NONMEMs) to deal with TTE data. Therefore, this tutorial simply explains how to analyze TTE data using NONMEM. We show how to write the code and evaluate the model. We also provide an example of a hands-on model for training.

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来源期刊

Translational and Clinical Pharmacology Medicine-Pharmacology (medical)

CiteScore

1.60

自引率

11.10%

发文量

期刊介绍： Translational and Clinical Pharmacology (Transl Clin Pharmacol, TCP) is the official journal of the Korean Society for Clinical Pharmacology and Therapeutics (KSCPT). TCP is an interdisciplinary journal devoted to the dissemination of knowledge relating to all aspects of translational and clinical pharmacology. The categories for publication include pharmacokinetics (PK) and drug disposition, drug metabolism, pharmacodynamics (PD), clinical trials and design issues, pharmacogenomics and pharmacogenetics, pharmacometrics, pharmacoepidemiology, pharmacovigilence, and human pharmacology. Studies involving animal models, pharmacological characterization, and clinical trials are appropriate for consideration.