PFIM 4.0: new features for optimal design in nonlinear mixed effects models using R
France Mentré (1), Thu Thuy Nguyen (1), Giulia Lestini (1), Cyrielle Dumont (1) and the PFIM group
(1) IAME, UMR 1137, INSERM and University Paris Diderot, 75018 Paris, France
Objectives: To extend PFIM, the only tool for population optimal design in R, with several new features.
Methods: Model based optimal design approaches are increasingly used in population pharmacokinetic/ pharmacodynamics (PKPD) . These approaches rely on the Fisher information matrix (FIM) for nonlinear mixed effect models and are a good alternative to clinical trial simulation. Several software tools are available and were recently compared . They all incorporate a PKPD library of models and model defined by differential equations. PFIM (www.pfim.biostat.fr) is available since 2001 and was extended in version 3 to multi-response models, inter-occasion variability, discrete covariates with prediction of power of Wald test .
We released in spring 2014 the version 4 of PFIM with several new features that we applied on several PKPD examples.
Results: For population designs, optimization can be done with fixed parameters or fixed sampling times. Previous information already obtained can be assumed and loaded through a predicted or an observed FIM. This is crucial to performed adaptive designs which are a strong requirement in drug industry and one of the task of the DDMoRe project .
Additional features for design in Bayesian estimation of individual parameters were added. The Bayesian information matrix was implemented. Design for MAP can be evaluated or optimized . The predicted shrinkage is also reported . We show with various examples the influence of design on shrinkage. This is a useful feature to select informative sampling times in therapeutic drug monitoring.
Conclusions: This new version of PFIM fulfilled some of the needs expressed in industry . The examples again showed the importance of model based optimal design to predict good studies and anticipate ‘fatal’ ones.
This work was supported partly by the DDMoRe project (www.ddmore.eu).
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