Transl Clin Pharmacol.
2022 Jun;30(2):75-82. 10.12793/tcp.2022.30.e8.
A simple time-to-event model with NONMEM featuring right-censoring
- Affiliations
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- 1College of Pharmacy, Chungnam National University, Daejeon 34134, Korea
- 2Department of Bio-AI convergence, Chungnam National University, Daejeon 34134, Korea
- 3Department of Clinical Pharmacology and Therapeutics, Asan Medical Center, University of Ulsan, Seoul 05505, Korea
- KMID: 2538868
- DOI: http://doi.org/10.12793/tcp.2022.30.e8
Abstract
- 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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