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      NONMEM Tutorial Part II: Estimation Methods and Advanced Examples

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      Author(s): 1 ,
      Publication date (Electronic):
      Journal: CPT: Pharmacometrics & Systems Pharmacology
      Publisher: John Wiley and Sons Inc.

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          Abstract

          In this second tutorial on NONMEM, the examples of typical pharmacokinetic/pharmacodynamic modeling problems that occur in the pharmaceutical field will be presented, which the reader can use as a template for his or her own modeling endeavors. Each of the problems presented is challenging in some way, and the logic behind setting up each problem is discussed. Logical concepts of the problem itself as well as the technical aspect of how to set it up in NONMEM are described and demonstrated. The concepts behind the various estimation algorithms will first be described to allow the user a better understanding of how to use them.

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          Computing normalised prediction distribution errors to evaluate nonlinear mixed-effect models: the npde add-on package for R.

          Emmanuelle Comets, Karl Brendel, France Mentré (2008)
          Pharmacokinetic/pharmacodynamic data are often analysed using nonlinear mixed-effect models, and model evaluation should be an important part of the analysis. Recently, normalised prediction distribution errors (npde) have been proposed as a model evaluation tool. In this paper, we describe an add-on package for the open source statistical package R, designed to compute npde. npde take into account the full predictive distribution of each individual observation and handle multiple observations within subjects. Under the null hypothesis that the model under scrutiny describes the validation dataset, npde should follow the standard normal distribution. Simulations need to be performed before hand, using for example the software used for model estimation. We illustrate the use of the package with two simulated datasets, one under the true model and one with different parameter values, to show how npde can be used to evaluate models. Model estimation and data simulation were performed using NONMEM version 5.1.
          Article 0 comments Cited 145 times – based on 0 reviews     Review now
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            Likelihood based approaches to handling data below the quantification limit using NONMEM VI.

            Christine Ahn, O. Karlsson, Jennifer A. Dunne … (2008)
            To evaluate the likelihood-based methods for handling data below the quantification limit (BQL) using new features in NONMEM VI. A two-compartment pharmacokinetic model with first-order absorption was chosen for investigation. Methods evaluated were: discarding BQL observations (M1), discarding BQL observations but adjusting the likelihood for the remaining data (M2), maximizing the likelihood for the data above the limit of quantification (LOQ) and treating BQL data as censored (M3), and like M3 but conditioning on the observation being greater than zero (M4). These four methods were compared using data simulated with a proportional error model. M2, M3, and M4 were also compared using data simulated from a positively truncated normal distribution. Successful terminations and bias and precision of parameter estimates were assessed. For the data simulated with a proportional error model, the overall performance was best for M3 followed by M2 and M1. M3 and M4 resulted in similar estimates in analyses without log transformation. For data simulated with the truncated normal distribution, M4 performed better than M3. Analyses that maximized the likelihood of the data above the LOQ and treated BQL data as censored provided the most accurate and precise parameter estimates.
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              Conditional weighted residuals (CWRES): a model diagnostic for the FOCE method.

              Andrew C Hooker, Christine E Staatz, Mats O Karlsson (2007)
              Population model analyses have shifted from using the first order (FO) to the first-order with conditional estimation (FOCE) approximation to the true model. However, the weighted residuals (WRES), a common diagnostic tool used to test for model misspecification, are calculated using the FO approximation. Utilizing WRES with the FOCE method may lead to misguided model development/evaluation. We present a new diagnostic tool, the conditional weighted residuals (CWRES), which are calculated based on the FOCE approximation. CWRES are calculated as the FOCE approximated difference between an individual's data and the model prediction of that data divided by the root of the covariance of the data given the model. Using real and simulated data the CWRES distributions behave as theoretically expected under the correct model. In contrast, in certain circumstances, the WRES have distributions that greatly deviate from the expected, falsely indicating model misspecification. CWRES/WRES comparisons can also indicate if the FOCE estimation method will improve the results of an FO model fit to data. Utilization of CWRES could improve model development and evaluation and give a more accurate picture of if and when a model is misspecified when using the FO or FOCE methods.
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                Author and article information

                Contributors
                Robert J. Bauer: robert.bauer@iconplc.com
                Journal
                Journal ID (nlm-ta): CPT Pharmacometrics Syst Pharmacol
                Journal ID (iso-abbrev): CPT Pharmacometrics Syst Pharmacol
                Journal ID (doi): 10.1002/(ISSN)2163-8306
                Journal ID (publisher-id): PSP4
                Title: CPT: Pharmacometrics & Systems Pharmacology
                Publisher: John Wiley and Sons Inc. (Hoboken )
                ISSN (Electronic): 2163-8306
                Publication date (Electronic): 21 June 2019
                Publication date (Print): August 2019
                Volume: 8
                Issue: 8 ( doiID: 10.1002/psp4.v8.8 )
                Pages: 538-556
                Affiliations
                [ 1 ] Pharmacometrics R&D ICON Clinical Research LLC Gaithersburg Maryland USA
                Author notes
                [*] [* ]Correspondence: Robert J. Bauer ( robert.bauer@ 123456iconplc.com )
                Article
                Publisher ID: PSP412422
                DOI: 10.1002/psp4.12422
                PMC ID: 6709422
                PubMed ID: 31044558
                SO-VID: 489233f3-6784-4e5b-900e-7181097e4bc9
                Copyright © © 2019. ICON Clinical Research LLC. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of the American Society for Clinical Pharmacology & Therapeutics.
                License:

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                Date received : 16 January 2019
                Date accepted : 09 April 2019
                Page count
                Figures: 2, Tables: 7, Pages: 19, Words: 12936
                Categories
                Subject: Tutorial
                Subject: Tutorial
                Subject: Tutorials
                Custom metadata
                source-schema-version-number 2.0
                component-id psp412422
                cover-date August 2019
                details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.7 mode:remove_FC converted:26.08.2019

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