PFIM

Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 29/08/12
  • Minor correction: 29/08/12
Keywords

PFIM : population design evaluation and optimisation

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System: Windows
  • Current version: PFIM INTERFACE 3.1 and PFIM 3.2.2 - March 2011
  • License(s): GPL
  • Status: stable release, under development
  • Support: maintained, ongoing development
  • Designer(s): Caroline Bazzoli, Thu Thuy Nguyen, Anne Dubois, Sylvie Retout, Emanuelle Comets, Hervé Le Nagard, France Mentré
  • Contact designer(s): caroline.bazzoli@imag.fr, thu-thuy.nguyen@inserm.fr, france.mentre@inserm.fr
  • Laboratory, service: INSERM-U738, LJK

 

General software features

PFIM (Population Fisher Information Matrix) is a R function dedicated to evaluation and optimisation of designs (number of subjects, number of samples per subject and their allocation in time) for nonlinear mixed effects models (population approach). This function is based on the developpement of an approximation of the Fisher information matrix in these models.

Two latest versions of PFIM are currently available :

  • a graphical user interface package using the R software (PFIM Interface 3.1),
  • an R script version (PFIM 3.2.2) requiring some knowledge in R use but which benefits of the latest methodological developments performed in the research team.
Context in which the software is used

PFIM is mainly used to design informative studies in pharmacology for the analyses of dose-concentration-effect (pharmacokinetics/pharmacodynamics) relationtionships of drugs.

PFIM is subject to statistical research (developement of the Fihser Infromation matrix, UMR 738 INSERM-Université Paris-Diderot) but also to pharmacology research.

Publications related to the software

Methodology

  • Nguyen TT, Bazzoli C, Mentré F. Design evaluation and optimisation in crossover pharmacokinetic studies analysed by nonlinear mixed effects models, Nguyen TT, Bazzoli C, Mentré F, 2011, [Epub ahead of print].
  • Bazzoli C, Retout S, Mentré F. Design evaluation and optimisation in multiple response nonlinear mixed effect models: PFIM 3.0, Computer Methods and Programs in Biomedicine, 2010, 98 : 55-65.
  • Retout S, Comets E, Bazzoli C, Mentré F. Design optimisation in nonlinear mixed effects models using cost functions:application to a joint model of infliximab and methotrexate pharmacokinetics, Communication in Statistics: Theory and Methods, 2009, 38 : 3351–3368.
  • Bazzoli C, Retout S, Mentré F. Fisher information matrix for nonlinear mixed effects multiple response models: evaluation of the appropriateness of the first order linearization using a pharmacokinetic/pharmacodynamic model, Statistics in Medicine, 2009, 28 : 1940-1956.
  • Retout S, Comets E, Samson A, Mentré F. Design in nonlinear mixed effects models: optimization using the Fedorov-Wynn algorithm and power of the Wald test for binary covariates, Statistics in Medicine, 2007, 26: 5162-5179.
  • Retout S, Mentré F. Optimisation of individual and population designs using Splus, Journal of Pharmacokinetic and Pharmacodynamics, 2003, 30: 417-443.
  • Retout S, Mentré F. Further developments of the Fisher information matrix in nonlinear mixed-effects models with evaluation in population pharmacokinetics, Journal of Biopharmaceutical Statistics, 2003, 13: 209-227.
  • Retout S, Mentré F, Bruno R. Fisher information matrix for nonlinear mixed-effects models: evaluation and application for optimal design of enoxaparin population, Statistics in Medicine, 2002, 21: 2623-2639.
  • Retout S, Duffull S, Mentré F. Development and implementation of the population Fisher information matrix for evaluation of population pharmacokinetic designs, Computer Methods and Programs in Biomedicine, 2001, 65: 141-151.
  • Mentré F, Mallet A, Baccar D. Optimal design in random-effects regression models, Biometrika, 1997, 84 : 429-442.

Applications of PFIM

  • Delavenne X, Zufferey P, Nguyen P, Rosencher N, Samama C.M,Bazzoli C, Mismett P, Laporte S. Pharmacokinetics of fondaparinux 1.5 mg once daily in a real-world cohort of patients with renal impairment undergoing major orthopaedic surgery. Pharmacokinetics and disposition, 2012, [Epub ahead of print].
  • Sherwin CM, Ding L, Kaplan J, Spigarelli MG, Vinks AA. Optimal study design for pioglitazone in septic pediatric patients. Journal of Pharmacokinetics and Pharmacodynamics, 2011, 38 : 433-447.
  • Guedj J, Bazzoli C, Neuman A.U, Mentré F. Design evaluation and optimization for models of hepatitis C
    viral dynamics. Statistics in Medicine, 2011, 30 : 1045-1056.
  • Bazzoli C, Retout S, Mentré F. Design evaluation and optimisation in multiple response nonlinear mixed effect models: PFIM 3.0, Computer Methods and Programs in Biomedicine, 2010, 98 : 55-65.
  • Retout, S., Comets, E., Bazzoli, C. et Mentré, F. Design optimisation in nonlinear mixed effects models using cost functions: application to a joint model of infliximab and methotrexate pharmacokinetics, Communication in
    Statistics: Theory and Methods, 2009, 38 : 3351–3368.
  • Bazzoli C, Retout S, Mentré F. Fisher information matrix for nonlinear mixed effects multiple response models: evaluation of the appropriateness of the first order linearization using a pharmacokinetic/pharmacodynamic model, Statistics in Medicine, 2009, 28 : 1940-1956.
  • Retout S, Comets E, Samson A, Mentré F. Design in nonlinear mixed effects models: optimization using the Fedorov-Wynn algorithm and power of the Wald test for binary covariates, Statistics in Medicine, 2007, 26 : 5162-5179.