Open AccessA simple time-to-event model with NONMEM featuring right-censoring
Author(s) -
Quyen Thi Tran,
Jung-Woo Chae,
KyunSeop Bae,
Hwi-yeol Yun
Publication year - 2022
Publication title -
translational and clinical pharmacology/translational and clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.166
H-Index - 6
eISSN - 2383-5427
pISSN - 2289-0882
DOI - 10.12793/tcp.2022.30.e8
Subject(s) - nonmem , computer science , censoring (clinical trials) , parametric model , parametric statistics , event data , simple (philosophy) , event (particle physics) , code (set theory) , population , data modeling , statistics , medicine , programming language , database , mathematics , philosophy , physics , environmental health , epistemology , quantum mechanics , set (abstract data type)
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.