Min-Gul Kim; Dong-Seok Yim; Kyun-Seop Bae

doi:10.12793/tcp.2015.23.1.1

Journal List > Transl Clin Pharmacol > v.23(1) > 1082602

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Kim, Yim, and Bae: R-based reproduction of the estimation process hidden behind NONMEM® Part 1: first-order approximation method

Original Article

Translational and Clinical Pharmacology 2015;23(1):1-7.

Published online: 30 June 2015

DOI: https://doi.org/10.12793/tcp.2015.23.1.1

R-based reproduction of the estimation process hidden behind NONMEM® Part 1: first-order approximation method

Min-Gul Kim¹, Dong-Seok Yim², Kyun-Seop Bae^3,^4,^∗

¹Clinical Pharmacology Unit, Biomedical Research Institute, Chonbuk National University Hospital, Jeonju 561–712, Republic of Korea

²PIPET (Pharmacometrics Institute for Practical Education and Training), The Catholic University of Korea, Seoul 137–701, Republic of Korea

³Department of Clinical Pharmacology and Therapeutics, Asan Medical Center, Seoul 138–736, Republic of Korea

⁴University of Ulsan College of Medicine, Seoul 138–736, Republic of Korea

^∗Correspondence: K. S. Bae; Tel: +82-2-3010-4611, Fax: +82-2-3010-4623, E-mail: ksbae@amc.seoul.kr

Received 28 May 2015 Revised 12 June 2015 Accepted 15 June 2015

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

NONMEM® is the most-widely used nonlinear mixed effects modelling tool introduced into population PK/PD analysis. Even though thousands of pharmaceutical scientists utilize NONMEM® routinely for their data analysis, the various estimation methods implemented in NONMEM® remain a mystery for most users due to the complex statistical and mathematical derivations underlying the algorithm used in NONMEM®. In this tutorial, we demonstrated how to directly obtain the objective function value and post hoc η for the first order approximation method by the use of R. We hope that this tutorial helps pharmacometricians understand the underlying estimation process of nonlinear mixed effects modelling.

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Keywords: NONMEM, modelling, First order approximation, R

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