التفاصيل البيبلوغرافية
| العنوان: |
Pfim 4. 0, an extended r program for design evaluation and optimization in nonlinear mixed-effect models |
| المؤلفون: |
Dumont, Cyrielle, Lestini, Giulia, Le Nagard, Herve, Mentre, France, Comets, Emmanuelle, Thu Th,uy Nguyen |
| المساهمون: |
CHU Lille, Université de Lille, Santé publique : épidémiologie et qualité des soins - EA 2694, 221576|||Evaluation des technologies de santé et des pratiques médicales - ULR 2694 METRICS (VALID), Infection, Anti-microbiens, Modélisation, Evolution IAME (UMR_S_1137 / U1137), METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694 |
| سنة النشر: |
2024 |
| المجموعة: |
LillOA (Lille Open Archive - Université de Lille) |
| مصطلحات موضوعية: |
Design, PFIM, Nonlinear mixed-effect model, Fisher information matrix, D-optimality |
| الوصف: |
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. 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. 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 ... |
| نوع الوثيقة: |
article in journal/newspaper |
| وصف الملف: |
application/octet-stream |
| اللغة: |
English |
| Relation: |
Computer Methods and Programs in Biomedicine; Comput. Meth. Programs Biomed.; http://hdl.handle.net/20.500.12210/17274 |
| الاتاحة: |
https://hdl.handle.net/20.500.12210/17274 |
| Rights: |
info:eu-repo/semantics/openAccess |
| رقم الانضمام: |
edsbas.5CFBC479 |
| قاعدة البيانات: |
BASE |