PFIM-package {PFIM}R Documentation

Fisher Information matrix for design evaluation/optimization for nonlinear mixed effects models.

Description

Evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. Methods used in the package refer to Mentré F, Mallet A, Baccar D (1997) doi:10.1093/biomet/84.2.429, Retout S, Comets E, Samson A, Mentré F (2007) doi:10.1002/sim.2910, Bazzoli C, Retout S, Mentré F (2009) doi:10.1002/sim.3573, Le Nagard H, Chao L, Tenaillon O (2011) doi:10.1186/1471-2148-11-326, Combes FP, Retout S, Frey N, Mentré F (2013) doi:10.1007/s11095-013-1079-3 and Seurat J, Tang Y, Mentré F, Nguyen TT (2021) doi:10.1016/j.cmpb.2021.106126.

Description

Nonlinear mixed effects models (NLMEM) are widely used in model-based drug development and use to analyze longitudinal data. The use of the "population" Fisher Information Matrix (FIM) is a good alternative to clinical trial simulation to optimize the design of these studies. PFIM 6.0 was released in 2023. The present version, PFIM 6.0, is an R package that uses the S4 object system for evaluating and/or optimizing population designs based on FIM in NLMEMs.

This version of PFIM now includes a library of models implemented also using the object oriented system S4 of R. This library contains two libraries of pharmacokinetic (PK) and/or pharmacodynamic (PD) models. The PK library includes model with different administration routes (bolus, infusion, first-order absorption), different number of compartments (from 1 to 3), and different types of eliminations (linear or Michaelis-Menten). The PD model library, contains direct immediate models (e.g. Emax and Imax) with various baseline models, and turnover response models. The PK/PD models are obtained with combination of the models from the PK and PD model libraries. PFIM handles both analytical and ODE models and offers the possibility to the user to define his/her own model(s). In PFIM 6.0, the FIM is evaluated by first order linearization of the model assuming a block diagonal FIM as in [3]. The Bayesian FIM is also available to give shrinkage predictions [4]. PFIM 6.0 includes several algorithms to conduct design optimization based on the D-criterion, given design constraints : the simplex algorithm (Nelder-Mead) [5], the multiplicative algorithm [6], the Fedorov-Wynn algorithm [7], PSO (Particle Swarm Optimization) and PGBO (Population Genetics Based Optimizer) [9].

Documentation

Documentation and user guide are available at http://www.pfim.biostat.fr/

Validation

PFIM 6.0 also provides quality control with tests and validation using the evaluated FIM to assess the validity of the new version and its new features. Finally, PFIM 6.0 displays all the results with both clear graphical form and a data summary, while ensuring their easy manipulation in R. The standard data visualization package ggplot2 for R is used to display all the results with clear graphical form [10]. A quality control using the D-criterion is also provided.

Organization of the source files in the /R folder

PFIM 6.0 contains a hierarchy of S4 classes with corresponding methods and functions serving as constructors. All of the source code related to the specification of a certain class is contained in a file named [Name_of_the_class]-Class.R. These classes include:

Content of the source code and files in the /R folder

Class Administration

Class AdministrationConstraints

Class Arm

Class BayesianFim

Class Combined1

Class Constant

Class Design

Class Distribution

Class Evaluation

Class FedorovWynnAlgorithm

Class FedorovWynnAlgorithm

Class Fim

Class GenericMethods

Class IndividualFim

Class LibraryOfModels

Class LibraryOfPKPDModels

Class LogNormal

Class Model

Class ModelAnalytic

Class ModelAnalyticBolus

Class ModelAnalyticBolusSteadyState

Class ModelBolus

Class ModelError

Class ModelInfusion

Class ModelODE

Class ModelODEBolus

Class ModelODEDoseInEquations

Class ModelODEDoseNotInEquations

Class ModelODEInfusion

Class ModelODEInfusionDoseInEquations

Class ModelParameter

Class MultiplicativeAlgorithm

Class Normal

Class Optimization

Class PFIMProject

Class PGBOAlgorithm

Class PlotEvaluation

Class PopulationFim

Class Proportional

Class PSOAlgorithm

Class SamplingTimeConstraints

Class SamplingTimes

Class SimplexAlgorithm

Author(s)

Maintainer: Romain Leroux romain.leroux@inserm.fr

Authors:

Other contributors:

References

[1] Dumont C, Lestini G, Le Nagard H, Mentré F, Comets E, Nguyen TT, et al. PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models. Comput Methods Programs Biomed. 2018;156:217-29.

[2] Chambers JM. Object-Oriented Programming, Functional Programming and R. Stat Sci. 2014;29:167-80.

[3] Mentré F, Mallet A, Baccar D. Optimal Design in Random-Effects Regression Models. Biometrika. 1997;84:429-42.

[4] Combes FP, Retout S, Frey N, Mentré F. Prediction of shrinkage of individual parameters using the Bayesian information matrix in nonlinear mixed effect models with evaluation in pharmacokinetics. Pharm Res. 2013;30:2355-67.

[5] Nelder JA, Mead R. A simplex method for function minimization. Comput J. 1965;7:308-13.

[6] Seurat J, Tang Y, Mentré F, Nguyen, TT. Finding optimal design in nonlinear mixed effect models using multiplicative algorithms. Computer Methods and Programs in Biomedicine, 2021.

[7] Fedorov VV. Theory of Optimal Experiments. Academic Press, New York, 1972.

[8] Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. Proc. of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, 4-6 October 1995, 39-43.

[9] Le Nagard H, Chao L, Tenaillon O. The emergence of complexity and restricted pleiotropy in adapting networks. BMC Evol Biol. 2011;11:326.

[10] Wickham H. ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag New York, 2016.

See Also

Useful links:

[Package PFIM version 6.0.3 Index]