PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models. (March 2018) - Dumont, Cyrielle; Lestini, Giulia; Le Nagard, Hervé; Mentré, France; Comets, Emmanuelle; Nguyen, Thu Thuy; group, for the PFIM
Highlights: PFIM 4.0 is the newest version of the R program PFIM for design evaluation and optimization in longitudinal studies, including a pharmacokinetic/pharmacodynamic library of models and several new features. PFIM 4.0 accommodates models including random effects for both between and within-subject variability as well as discrete covariates. Optimization can be performed assuming some fixed parameters or some fixed sampling times. Previously obtained results, summarized in a Fisher information matrix, can be taken into account in evaluation or optimization of one-group protocols, enabling the use of PFIM 4.0 for adaptive designs. Additional features based on the Bayesian individual information matrix have been implemented, enabling design evaluation and optimization for Maximum A Posteriori estimation of individual parameters. Abstract: Background and Objective: Nonlinear mixed-effect models (NLMEMs) are increasingly used for the analysis of longitudinal studies during drug development. When designing these studies, the expected Fisher information matrix (FIM) can be used instead of performing time-consuming clinical trial simulations. The function PFIM is the first tool for design evaluation and optimization that has been developed in R. In this article, we present an extended version, PFIM 4.0, which includes several new features. Methods: Compared with version 3.0, PFIM 4.0 includes a more complete pharmacokinetic/pharmacodynamic library of models and accommodates models including additional random effects for inter-occasion variability as well as discrete covariates. A new input method has been added to specify user-defined models through an R function. Optimization can be performed assuming some fixed parameters or some fixed sampling times. New outputs have been added regarding the FIM such as eigenvalues, conditional numbers, and the option of saving the matrix obtained after evaluation or optimization. Previously obtained results, which are summarized in a FIM, can be taken into account in evaluation or optimization of one-group protocols. This feature enables the use of PFIM for adaptive designs. The Bayesian individual FIM has been implemented, taking into account a priori distribution of random effects. Designs for maximum a posteriori Bayesian estimation of individual parameters can now be evaluated or optimized and the predicted shrinkage is also reported. It is also possible to visualize the graphs of the model and the sensitivity functions without performing evaluation or optimization. Results: The usefulness of these approaches and the simplicity of use of PFIM 4.0 are illustrated by two examples: (i) an example of designing a population pharmacokinetic study accounting for previous results, which highlights the advantage of adaptive designs; (ii) an example of Bayesian individual design optimization for a pharmacodynamic study, showing that the Bayesian individual FIM can be a useful tool in therapeutic drug monitoring, allowing efficient prediction of estimation precision and shrinkage for individual parameters. Conclusion: PFIM 4.0 is a useful tool for design evaluation and optimization of longitudinal studies in pharmacometrics and is freely available athttp://www.pfim.biostat.fr .