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Mats O Karlsson - One of the best experts on this subject based on the ideXlab platform.
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handling underlying discrete variables with bivariate mixed hidden markov models in NONMEM
Journal of Pharmacokinetics and Pharmacodynamics, 2019Co-Authors: Ari Brekkan, Mats O Karlsson, Siv Jonsson, Elodie L PlanAbstract:Non-linear mixed effects models typically deal with stochasticity in observed processes but models accounting for only observed processes may not be the most appropriate for all data. Hidden Markov models (HMMs) characterize the relationship between observed and hidden variables where the hidden variables can represent an underlying and unmeasurable disease status for example. Adding stochasticity to HMMs results in mixed HMMs (MHMMs) which potentially allow for the characterization of variability in unobservable processes. Further, HMMs can be extended to include more than one observation source and are then multivariate HMMs. In this work MHMMs were developed and applied in a chronic obstructive pulmonary disease example. The two hidden states included in the model were remission and exacerbation and two observation sources were considered, patient reported outcomes (PROs) and forced expiratory volume (FEV1). Estimation properties in the software NONMEM of model parameters were investigated with and without random and covariate effect parameters. The influence of including random and covariate effects of varying magnitudes on the parameters in the model was quantified and a power analysis was performed to compare the power of a single bivariate MHMM with two separate univariate MHMMs. A bivariate MHMM was developed for simulating and analysing hypothetical COPD data consisting of PRO and FEV1 measurements collected every week for 60 weeks. Parameter precision was high for all parameters with the exception of the variance of the transition rate dictating the transition from remission to exacerbation (relative root mean squared error [RRMSE] > 150%). Parameter precision was better with higher magnitudes of the transition probability parameters. A drug effect was included on the transition rate probability and the precision of the drug effect parameter improved with increasing magnitude of the parameter. The power to detect the drug effect was improved by utilizing a bivariate MHMM model over the univariate MHMM models where the number of subject required for 80% power was 25 with the bivariate MHMM model versus 63 in the univariate MHMM FEV1 model and > 100 in the univariate MHMM PRO model. The results advocates for the use of bivariate MHMM models when implementation is possible.
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evaluation of bias precision robustness and runtime for estimation methods in NONMEM 7
Journal of Pharmacokinetics and Pharmacodynamics, 2014Co-Authors: Asa Johansson, Andrew C Hooker, Sebastian Ueckert, Elodie L Plan, Mats O KarlssonAbstract:NONMEM is the most widely used software for population pharmacokinetic (PK)-pharmacodynamic (PD) analyses. The latest version, NONMEM 7 (NM7), includes several sampling-based estimation methods in addition to the classical methods. In this study, performance of the estimation methods available in NM7 was investigated with respect to bias, precision, robustness and runtime for a diverse set of PD models. Simulations of 500 data sets from each PD model were reanalyzed with the available estimation methods to investigate bias and precision. Simulations of 100 data sets were used to investigate robustness by comparing final estimates obtained after estimations starting from the true parameter values and initial estimates randomly generated using the CHAIN feature in NM7. Average estimation time for each algorithm and each model was calculated from the runtimes reported by NM7. The method giving the lowest bias and highest precision across models was importance sampling, closely followed by FOCE/LAPLACE and stochastic approximation expectation-maximization. The methods relative robustness differed between models and no method showed clear superior performance. FOCE/LAPLACE was the method with the shortest runtime for all models, followed by iterative two-stage. The Bayesian Markov Chain Monte Carlo method, used in this study for point estimation, performed worst in all tested metrics.
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modeling and simulation workbench for NONMEM tutorial on pirana psn and xpose
CPT: pharmacometrics & systems pharmacology, 2013Co-Authors: Ron J Keizer, Mats O Karlsson, Andrew C HookerAbstract:Several software tools are available that facilitate the use of the NONMEM software and extend its functionality. This tutorial shows how three commonly used and freely available tools, Pirana, PsN, and Xpose, form a tightly integrated workbench for modeling and simulation with NONMEM. During the tutorial, we provide some guidance on what diagnostics we consider most useful in pharmacokinetic model development and how to construct them using these tools.
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standard error of empirical bayes estimate in NONMEM vi
The Korean Journal of Physiology and Pharmacology, 2012Co-Authors: Dongwoo Kang, Kyunseop Bae, Brett E Houk, Radojka M Savic, Mats O KarlssonAbstract:The pharmacokinetics/pharmacodynamics analysis software NONMEM® output provides model parameter estimates and associated standard errors. However, the standard error of empirical Bayes estimates of inter-subject variability is not available. A simple and direct method for estimating standard error of the empirical Bayes estimates of inter-subject variability using the NONMEM® VI internal matrix POSTV is developed and applied to several pharmacokinetic models using intensively or sparsely sampled data for demonstration and to evaluate performance. The computed standard error is in general similar to the results from other post-processing methods and the degree of difference, if any, depends on the employed estimation options.
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performance in population models for count data part i maximum likelihood approximations
Journal of Pharmacokinetics and Pharmacodynamics, 2009Co-Authors: Elodie L Plan, Alan Maloney, Inaki F Troconiz, Mats O KarlssonAbstract:There has been little evaluation of maximum likelihood approximation methods for non-linear mixed effects modelling of count data. The aim of this study was to explore the estimation accuracy of population parameters from six count models, using two different methods and programs. Simulations of 100 data sets were performed in NONMEM for each probability distribution with parameter values derived from a real case study on 551 epileptic patients. Models investigated were: Poisson (PS), Poisson with Markov elements (PMAK), Poisson with a mixture distribution for individual observations (PMIX), Zero Inflated Poisson (ZIP), Generalized Poisson (GP) and Negative Binomial (NB). Estimations of simulated datasets were completed with Laplacian approximation (LAPLACE) in NONMEM and LAPLACE/Gaussian Quadrature (GQ) in SAS. With LAPLACE, the average absolute value of the bias (AVB) in all models was 1.02% for fixed effects, and ranged 0.32–8.24% for the estimation of the random effect of the mean count (λ). The random effect of the overdispersion parameter present in ZIP, GP and NB was underestimated (−25.87, −15.73 and −21.93% of relative bias, respectively). Analysis with GQ 9 points resulted in an improvement in these parameters (3.80% average AVB). Methods implemented in SAS had a lower fraction of successful minimizations, and GQ 9 points was considerably slower than 1 point. Simulations showed that parameter estimates, even when biased, resulted in data that were only marginally different from data simulated from the true model. Thus all methods investigated appear to provide useful results for the investigated count data models.
Robert J Bauer - One of the best experts on this subject based on the ideXlab platform.
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NONMEM tutorial part i description of commands and options with simple examples of population analysis
CPT: pharmacometrics & systems pharmacology, 2019Co-Authors: Robert J BauerAbstract:In this tutorial, the various components of NONMEM® will be described, and basic steps of setting up NONMEM control stream files and data files will be demonstrated. Some basic concepts of nonlinear mixed effects modelling will be discussed, along with simple examples demonstrating how to use NONMEM to perform population analysis of clinical data. This article is protected by copyright. All rights reserved.
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NONMEM tutorial part ii estimation methods and advanced examples
CPT: pharmacometrics & systems pharmacology, 2019Co-Authors: Robert J BauerAbstract:In this second tutorial on NONMEM, examples of typical PK/PD modeling problems that occur in the pharmaceutical field will be presented, and which the reader can utilize as a template for his own modeling endeavors. Each of the problems presented are 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. This article is protected by copyright. All rights reserved.
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a survey of population analysis methods and software for complex pharmacokinetic and pharmacodynamic models with examples
Aaps Journal, 2007Co-Authors: Robert J Bauer, Serge Guzy, Chee NgAbstract:An overview is provided of the present population analysis methods and an assessment of which software packages are most appropriate for various PK/PD modeling problems. Four PK/PD example problems were solved using the programs NONMEM VI beta version, PDx-MCPEM, S-ADAPT, MONOLIX, and WinBUGS, informally assessed for reasonable accuracy and stability in analyzing these problems. Also, for each program we describe their general interface, ease of use, and abilities. We conclude with discussing which algorithms and software are most suitable for which types of PK/PD problems. NONMEM FO method is accurate and fast with 2-compartment models, if intra-individual and interindividual variances are small. The NONMEM FOCE method is slower than FO, but gives accurate population values regardless of size of intra- and interindividual errors. However, if data are very sparse, the NONMEM FOCE method can lead to inaccurate values, while the Laplace method can provide more accurate results. The exact EM methods (performed using S-ADAPT, PDx-MCPEM, and MONOLIX) have greater stability in analyzing complex PK/PD models, and can provide accurate results with sparse or rich data. MCPEM methods perform more slowly than NONMEM FOCE for simple models, but perform more quickly and stably than NONMEM FOCE for complex models. WinBUGS provides accurate assessments of the population parameters, standard errors and 95% confidence intervals for all examples. Like the MCPEM methods, WinBUGS's efficiency increases relative to NONMEM when solving the complex PK/PD models.
Ron J Keizer - One of the best experts on this subject based on the ideXlab platform.
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modeling and simulation workbench for NONMEM tutorial on pirana psn and xpose
CPT: pharmacometrics & systems pharmacology, 2013Co-Authors: Ron J Keizer, Mats O Karlsson, Andrew C HookerAbstract:Several software tools are available that facilitate the use of the NONMEM software and extend its functionality. This tutorial shows how three commonly used and freely available tools, Pirana, PsN, and Xpose, form a tightly integrated workbench for modeling and simulation with NONMEM. During the tutorial, we provide some guidance on what diagnostics we consider most useful in pharmacokinetic model development and how to construct them using these tools.
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pirana and pcluster a modeling environment and cluster infrastructure for NONMEM
Computer Methods and Programs in Biomedicine, 2011Co-Authors: Ron J Keizer, Michel Van Benten, Jos H Beijnen, Jan H M Schellens, Alwin D R HuitemaAbstract:Pharmacokinetic-pharmacodynamic modeling using non-linear mixed effects modeling (NONMEM) is a powerful yet challenging technique, as the software is generally accessed from the command line. A graphical user interface, Pirana, was developed that offers a complete modeling environment for NONMEM, enabling both novice and advanced users to increase efficiency of their workflow. Pirana provides features for the management and creation of model files, the overview of modeling results, creation of run reports and handling of datasets and output tables, and the running of custom R scripts on model output. Through the secure shell (SSH) protocol, Pirana can also be used to connect to Linux clusters (SGE, MOSIX) for distribution of workload. Modeling with NONMEM is computationally burdensome, which may be alleviated by distributing runs to computer clusters. A solution to this problem is offered here, called PCluster. This platform is easy to set up, runs in standard network environments, and can be extended with additional nodes if needed. The cluster supports the modeling toolkit Perl speaks NONMEM (PsN), and can include dedicated or non-dedicated PCs. A daemon script, written in Perl, was designed to run in the background on each node in the cluster, and to manage job distribution. The PCluster can be accessed from Pirana, and both software products have extensively been tested on a large academic network. The software is available under an open-source license.
Y Takaue - One of the best experts on this subject based on the ideXlab platform.
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Population pharmacokinetics of intravenous busulfan in patients undergoing hematopoietic stem cell transplantation
Bone Marrow Transplantation, 2006Co-Authors: H Takama, H Tanaka, D Nakashima, R Ueda, Y TakaueAbstract:A population pharmacokinetic analysis was performed in 30 patients who received an intravenous busulfan and cyclophosphamide regimen before hematopoietic stem cell transplantation. Each patient received 0.8 mg/kg as a 2 h infusion every 6 h for 16 doses. A total of 690 concentration measurements were analyzed using the nonlinear mixed effect model (NONMEM) program. A one-compartment model with an additive error model as an intraindividual variability including an interoccasion variability (IOV) in clearance (CL) was sufficient to describe the concentration–time profile of busulfan. Actual body weight (ABW) was found to be the determinant for CL and the volume of distribution ( V ) according to NONMEM analysis. In this limited study, the age (range 7–53 years old; median, 30 years old) had no significant effect on busulfan pharmacokinetics. For a patient weighting 60 kg, the typical CL and V were estimated to be 8.87 l/h and 33.8 l, respectively. The interindividual variability of CL and V were 13.6 and 6.3%, respectively. The IOV (6.6%) in CL was estimated to be less than the intraindividual variability. These results indicate high interpatient and intrapatient consistency of busulfan pharmacokinetics after intravenous administration, which may eliminate the requirement for pharmacokinetic monitoring.
Lu Wei - One of the best experts on this subject based on the ideXlab platform.
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pharmacodynamics of propofol administered by target controlled infusion in chinese elderly operation patients by NONMEM
The Chinese Journal of Clinical Pharmacology, 2007Co-Authors: Lu WeiAbstract:Objective To investigate the pharmacodynamics of propofol administered by target-controlled infusion(TCI)in Chinese elderly patients.Methods Thirty-two ASA Ⅰ-Ⅱ patients,undergoing selective lower abdominal operation were studied.Propofol was administered by TCI with Marsh parameter.The plasma concentration of propofol analyzed by reversed-phase HPLC with fluorescence detection.Pharmacokinetic and pharmacodynamic modeling was performed using NONMEM soft ware.Results Taken BIS as clinical index,the quantitative relationship between blood concentration and pharmacodynamics was analyzed by PK/PD link model with left-hysteresis loop.Conclusion Keo of elder patients in China was 0.13 min-1,EC50 was 3.77μg·mL-1.
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setting up population pharmacokinetics pharmacodynamics model of vpa in children with epilepsy by NONMEM software
Chinese Journal of Clinical Pharmacology and Therapeutics, 2004Co-Authors: Jiang Dechun, Lu WeiAbstract:AIM: To set up population pharmacokinetics/pharmacodynamics (PPK/PD) model of valproate (VPA) in children with epilepsy in China, and promote reasonable use of antiepileptic drugs(AEDs)in clinical practice. METHODS: Sparse data of VPA serum concentrations from 246 pediatric children were collected. These patients were divided into three groups: PPK-Model group ([WTBX]n=146), to calculate PPK parameter values of VPA and set up a PPK model; PPK-Valid group ([WTBX]n=100), to valid the PPK model; and PPD group ([WTBX]n=69), to set up PPK/PD model. Based on the data of PPK-Model group and PPK-Valid group, a PPK model of VPA in children with epilepsy in China was successfully set up by using NONMEM software by ourselves. Now, using the data of 69 patients in PPD group who were on VPA monotherapy and this PPK model, we set up PPK/PD model by NONMEM software. Efficacy of epilepsy treatment was divided into 5 grades according to the percentage of seizure frequency decreased (PSFD%): grade 1: PSFD% was 100%; grade 2: PSFD% was 75%-100%; grade 3: PSFD% was 50%-75%; grade 4: PSFD% was 25%-50%; grade 5: PSFD% was less than 25%. The quantitive relationship between the VPA serum concentrations and the probability for its efficacy score was characterized by Logistic regression analysis with NONMEM. RESULTS: Logistic regression analysis showed that, VPA serum concentrations and the probability for its efficacy grades 5, 4, 3, 2, and 1 were (23 μg·ml -1, 5, 50%), (30 μg·ml -1, 4, 32.3%), (50 μg·ml -1, 3, 26.3%), (65 μg·ml -1, 2, 36.5%), (78 μg·ml -1, 1, 50%), and (100 μg·ml -1, 1, 84.2%)respectively. CONCLUSION: A PPK/PD model of VPA in children with epilepsy in China is successfully established by using NONMEM software, and the probability of efficacy grade for any concentration can be calculated. It will be valuable to facilitate individualized dosage regimen.
