NONMEM

NONMEM® is a nonlinear mixed effects modelling tool used in population pharmacokinetic/pharmacodynamic analysis. NONMEM stands for NONlinear Mixed Effects Modeling. NONMEM is a computer program that is implemented in Fortran90/95. It solves pharmaceutical statistical problems in which within subject and between subjects variability is taken into account when fitting a pharmacokinetic and/or pharmacodynamic (PK/PD) model to data. The appropriate statistical analysis using the appropriate model helps pharmaceutical companies determine appropriate dosing strategies for their products, and increases their understanding of drug mechanisms and interactions.NONMEM software was originally developed by Lewis Sheiner and Stuart Beal and the NONMEM Project Group at the University of California, and has been used for over 30 years for population analysis by many pharmaceutical companies and the PK/PD modeling community. Its continued development and improvement by ICON Development Solutions assures pharmaceutical companies that they may continue to use the analysis tool with which they are familiar for present day pharmaceutical development.


References in zbMATH (referenced in 33 articles )

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  1. Karimi, Belhal; Lavielle, Marc; Moulines, Eric: f-SAEM: a fast stochastic approximation of the EM algorithm for nonlinear mixed effects models (2020)
  2. Saccomani, Maria Pia; Thomaseth, Karl: The union between structural and practical identifiability makes strength in reducing oncological model complexity: a case study (2018)
  3. Emmanuelle Comets; Audrey Lavenu; Marc Lavielle: Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm (2017) not zbMATH
  4. Grimm, Kevin J.; Ram, Nilam; Estabrook, Ryne: Growth modeling. Structural equation and multilevel modeling approaches (2017)
  5. Kim, Seong-Joon; Bae, Suk Joo: Degradation test plan for a nonlinear random-coefficients model (2017)
  6. Tomás, Elson; Vinga, Susana; Carvalho, Alexandra M.: Unsupervised learning of pharmacokinetic responses (2017)
  7. Verotta, Davide; Haagensen, Janus; Spormann, Alfred M.; Yang, Katherine: Mathematical modeling of biofilm structures using COMSTAT data (2017)
  8. Heinzl, Felix; Tutz, Gerhard: Additive mixed models with approximate Dirichlet process mixtures: the EM approach (2016)
  9. Ryan, Elizabeth G.; Drovandi, Christopher C.; Pettitt, Anthony N.: Simulation-based fully Bayesian experimental design for mixed effects models (2015)
  10. Vinogradova, Svetlana V.; Zhudenkov, Kirill V.; Benson, Neil; Van Der Graaf, Piet H.; Demin, Oleg V.; Karelina, Tatiana A.: Prediction of long-term treatment outcome in HCV following 24 day PEG-IFN alpha-2b therapy using population pharmacokinetic-pharmacodynamic mixture modeling and classification analysis (2015)
  11. Gudmand-Hoeyer, Johanne; Timmermann, Stine; Ottesen, Johnny T.: Patient-specific modeling of the neuroendocrine HPA-axis and its relation to depression: ultradian and circadian oscillations (2014)
  12. Wang, L.; Cao, J.; Ramsay, J. O.; Burger, D. M.; Laporte, C. J. L.; Rockstroh, J. K.: Estimating mixed-effects differential equation models (2014)
  13. Demidenko, Eugene: Mixed models. Theory and applications with R (2013)
  14. Wang, Jing: Dirichlet processes in nonlinear mixed effects models (2010)
  15. Antic, J.; Laffont, C. M.; Chafaï, D.; Concordet, D.: Comparison of nonparametric methods in nonlinear mixed effects models (2009)
  16. Crespi, Catherine M.; Boscardin, W. John: Bayesian model checking for multivariate outcome data (2009)
  17. Pillonetto, Gianluigi; de Nicolao, Giuseppe; Chierici, Marco; Cobelli, Claudio: Fast algorithms for nonparametric population modeling of large data sets (2009)
  18. Schlattmann, Peter: Medical applications of finite mixture models (2009)
  19. Freijer, Jan I.; Post, Teun M.; Ploeger, Bart A.; Dejongh, Joost; Danhof, Meindert: Application of the convection-dispersion equation to modelling oral drug absorption (2007)
  20. Neve, M.; De Nicolao, G.; Marchesi, L.: Nonparametric identification of population models via Gaussian processes (2007)

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