IAME, UMR 1137, INSERM and University Paris Diderot, Sorbonne Paris Cité, Paris, F-75018, France; University of Lille, EA 2694, Public Health: Epidemiology and Healthcare Quality, ILIS, Lille, F-59000, France.
IAME, UMR 1137, INSERM and University Paris Diderot, Sorbonne Paris Cité, Paris, F-75018, France.
Comput Methods Programs Biomed. 2018 Mar;156:217-229. doi: 10.1016/j.cmpb.2018.01.008. Epub 2018 Jan 11.
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 can be a useful tool in therapeutic drug monitoring, allowing efficient prediction of estimation precision and shrinkage for individual parameters.
PFIM 4.0 is a useful tool for design evaluation and optimization of longitudinal studies in pharmacometrics and is freely available at http://www.pfim.biostat.fr.
非线性混合效应模型（NLMEM）在药物开发期间的纵向研究分析中得到了越来越广泛的应用。在设计这些研究时，可以使用预期的 Fisher 信息矩阵（FIM）代替耗时的临床试验模拟。PFIM 函数是第一个在 R 中开发的用于设计评估和优化的工具。在本文中，我们介绍了一个扩展版本 PFIM 4.0，它包含了几个新特性。
与 3.0 版本相比，PFIM 4.0 包含了更完整的药代动力学/药效学模型库，并支持包括用于偶发变异性的额外随机效应以及离散协变量的模型。添加了一种新的输入方法，通过 R 函数指定用户定义的模型。可以在假定某些固定参数或某些固定采样时间的情况下进行优化。添加了有关 FIM 的新输出，例如特征值、条件数以及在评估或优化后保存获得的矩阵的选项。以前的结果（汇总在 FIM 中）可以在一个组协议的评估或优化中考虑。此功能允许使用 PFIM 进行自适应设计。实现了贝叶斯个体 FIM，考虑了随机效应的先验分布。现在可以评估或优化用于个体参数最大后验贝叶斯估计的设计，并且还报告了预测的收缩。还可以在不执行评估或优化的情况下可视化模型和灵敏度函数的图形。
通过两个示例说明了这些方法的有用性和 PFIM 4.0 的易用性：（i）一个考虑先前结果的群体药代动力学研究设计示例，突出了自适应设计的优势；（ii）一个用于药效学研究的贝叶斯个体设计优化示例，表明贝叶斯个体 FIM 可以成为治疗药物监测的有用工具，允许对个体参数的估计精度和收缩进行有效预测。
PFIM 4.0 是药代动力学中纵向研究设计评估和优化的有用工具，可在 http://www.pfim.biostat.fr 免费获得。
