Tran Quyen Thi, Chae Jung-Woo, Bae Kyun-Seop, Yun Hwi-Yeol
College of Pharmacy, Chungnam National University, Daejeon 34134, Korea.
Department of Bio-AI convergence, Chungnam National University, Daejeon 34134, Korea.
Transl Clin Pharmacol. 2022 Jun;30(2):75-82. doi: 10.12793/tcp.2022.30.e8. Epub 2022 Jun 15.
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.
在医疗保健场景中，事件发生时间（TTE）数据是常见的结果。通常采用参数方法来处理TTE数据，因为通过模拟可以轻松可视化不同的情况。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（NONMEM）来处理TTE数据。因此，本教程简单解释了如何使用NONMEM分析TTE数据。我们展示了如何编写代码和评估模型。我们还提供了一个用于培训的实际操作模型示例。
