1.Erratum: R-based reproduction of the estimation process hidden behind NONMEM Part 2: First-order conditional estimation
Translational and Clinical Pharmacology 2018;26(2):99-99
The equations on page 162 should be corrected.
2.Analytical solution of linear multi-compartment models with non-zero initial condition and its implementation with R
David Z D'ARGENIO ; Kyun Seop BAE
Translational and Clinical Pharmacology 2019;27(2):43-51
The analytical solution for multi-compartment models with a non-zero initial condition is complex because of the inter-compartmental transfer. An elegant solution and its implementation in the ‘wnl' R package can be useful in solving examples of textbooks and developing software of therapeutic drug monitoring, pharmacokinetic simulation, and parameter estimation. This solution uses Laplace transformation, convolution, matrix inversion, and the fact that the general solution of an inhomogeneous ordinary differential equation is the sum of a homogenous and a particular solution, together.
Drug Monitoring
3.Erratum: Development of R packages: ‘NonCompart’ and ‘ncar’ for noncompartmental analysis (NCA)
Hyungsub KIM ; Sungpil HAN ; Yong Soon CHO ; Seok Kyu YOON ; Kyun Seop BAE
Translational and Clinical Pharmacology 2018;26(3):141-141
There are some errors in the published article. The authors would like to make corrections in the original version of the article.
4.R-based reproduction of the estimation process hidden behind NONMEM® Part 2: First-order conditional estimation.
Translational and Clinical Pharmacology 2016;24(4):161-168
The first-order conditional estimation (FOCE) method is more complex than the first-order (FO) approximation method because it estimates the empirical Bayes estimate (EBE) for each iteration. By contrast, it is a further approximation of the Laplacian (LAPL) method, which uses second-order expansion terms. FOCE without INTERACTION can only be used for an additive error model, while FOCE with INTERACTION (FOCEI) can be used for any error model. The formula for FOCE without INTERACTION can be derived directly from the extension of the FO method, while the FOCE with INTERACTION method is a slight simplification of the LAPL method. Detailed formulas and R scripts are presented here for the reproduction of objective function values by NONMEM.
Bays
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Methods
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Reproduction*
5.Bioequivalence data analysis
Gowooni PARK ; Hyungsub KIM ; Kyun-Seop BAE
Translational and Clinical Pharmacology 2020;28(4):175-180
SAS® is commonly used for bioequivalence (BE) data analysis. R is a free and open software for general purpose data analysis, and is less frequently used than SAS® for BE data analysis. This tutorial explains how R can be used for BE data analysis to generate comparable results with SAS® . The main SAS® procedures for BE data analysis are PROC GLM and PROC MIXED, and the corresponding R main packages are “sasLM” and “nlme” respectively. For fixed effects only or balanced data, the SAS® PROC GLM and R “sasLM” provide good estimates; however, for a mixed-effects model with unbalanced data, the SAS® PROC MIXED and R “nlme” are better for providing estimates without bias. The SAS® and R scripts are provided for convenience.
6.Bioequivalence data analysis
Gowooni PARK ; Hyungsub KIM ; Kyun-Seop BAE
Translational and Clinical Pharmacology 2020;28(4):175-180
SAS® is commonly used for bioequivalence (BE) data analysis. R is a free and open software for general purpose data analysis, and is less frequently used than SAS® for BE data analysis. This tutorial explains how R can be used for BE data analysis to generate comparable results with SAS® . The main SAS® procedures for BE data analysis are PROC GLM and PROC MIXED, and the corresponding R main packages are “sasLM” and “nlme” respectively. For fixed effects only or balanced data, the SAS® PROC GLM and R “sasLM” provide good estimates; however, for a mixed-effects model with unbalanced data, the SAS® PROC MIXED and R “nlme” are better for providing estimates without bias. The SAS® and R scripts are provided for convenience.
7.A simple time-to-event model with NONMEM featuring right-censoring
Quyen Thi TRAN ; Jung-woo CHAE ; Kyun-Seop BAE ; Hwi-yeol YUN
Translational and Clinical Pharmacology 2022;30(2):75-82
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.
8.Implementation of Miettinen-Nurminen score method with or without stratification in R
Translational and Clinical Pharmacology 2022;30(3):155-162
Analysis of a 2 × 2 table for clinical data involves computing the point estimate and confidence interval for risk difference, relative risk, or odds ratio. While point estimates of these comparative parameters are uniquely defined, several statistical methods have been proposed to estimate the confidence interval for each parameter. The Miettinen-Nurminen (MN) score method is expected to be used increasingly over traditional interval estimation methods. The MN score method has not been previously implemented in R software for data with stratification. There is a need for a comprehensive software implementation of the MN score method. This article describes the implementation of the MN score method in the sasLM R software package. To demonstrate the usage of the sasLM functions introduced, recently published clinical data are provided as examples.
9.Likelihood interval for nonlinear regression
Translational and Clinical Pharmacology 2023;31(2):85-94
Wald confidence interval has been used as the conventional method of interval estimation for the parameters in nonlinear models. Because Wald confidence interval is symmetric around the point estimate, it does not reflect the asymmetry of the likelihood profile in nonlinear regression. In contrast, a likelihood interval is estimated directly from the likelihood profile and does reflect the shape of the likelihood profile. However, the lack of software for the estimation of likelihood intervals and visualization of likelihood profiles posed an obstacle to the use of likelihood intervals in nonlinear models. There was a need for software implementation to tackle these tasks. Likelihood interval estimation and likelihood profile plotting for nonlinear models had not been previously implemented in R software. This article describes the implementation of likelihood interval estimation and likelihood profile plotting in the wnl R software package. To demonstrate the usage of implemented functions, an example of fitting a nonlinear pharmacokinetic model to concentration-time data is presented.
10.R-based reproduction of the estimation process hidden behind NONMEM(R) Part 1: first-order approximation method.
Min Gul KIM ; Dong Seok YIM ; Kyun Seop BAE
Translational and Clinical Pharmacology 2015;23(1):1-7
NONMEM(R) is the most-widely used nonlinear mixed effects modelling tool introduced into population PK/PD analysis. Even though thousands of pharmaceutical scientists utilize NONMEM(R) routinely for their data analysis, the various estimation methods implemented in NONMEM(R) remain a mystery for most users due to the complex statistical and mathematical derivations underlying the algorithm used in NONMEM(R). In this tutorial, we demonstrated how to directly obtain the objective function value and post hoc eta 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.
Hope
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Reproduction*
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Statistics as Topic