Ibrahim Moustafa M A, Jonsson E Niclas, Bergstrand Martin
Pharmetheus AB, Uppsala, Sweden.
AAPS J. 2025 Apr 29;27(4):86. doi: 10.1208/s12248-025-01065-2.
The prediction-corrected visual predictive check (pcVPC) is an informative model diagnostic that can offer advantages over the standard visual predictive check (VPC) when heterogenous study designs and adaptive dosing are used. However, a drawback with these plots is that prediction correction often results in y-axis values and trends that are unintuitive, difficult to explain, and challenging to communicate even among experts. The reference-corrected visual predictive check (rcVPC) offers a solution to these problems by leveraging a user-defined set of independent variables, for a more intuitive model diagnostic and an efficient communication of results to a wider audience. The rcVPC methodology is based on the definition of a reference dataset. Simulations are conducted with this reference dataset and the observed dataset, and then the simulated and the observed dependent variables are normalized by the population prediction for the user-defined independent variables in the reference dataset. The opportunity to manipulate time in the reference dataset is a unique feature that gives rcVPC the ability to visually characterize exposure-response relationships with delayed effect onset. The rcVPC approach was compared to pcVPCs and traditional VPCs for a range of examples inspired by real data. The rcVPC methodology was demonstrated to offer a more intuitive interpretation and more effective guidance to model development in a way that is not possible for VPC or pcVPC plots.
预测校正视觉预测检查（pcVPC）是一种信息丰富的模型诊断方法，当使用异质性研究设计和适应性给药时，它比标准视觉预测检查（VPC）具有优势。然而，这些图的一个缺点是，预测校正通常会导致y轴值和趋势不直观、难以解释，甚至在专家之间进行交流也具有挑战性。参考校正视觉预测检查（rcVPC）通过利用一组用户定义的自变量，为这些问题提供了一个解决方案，从而实现更直观的模型诊断，并能更有效地将结果传达给更广泛的受众。rcVPC方法基于参考数据集的定义。使用该参考数据集和观察数据集进行模拟，然后通过参考数据集中用户定义自变量的总体预测对模拟和观察到的因变量进行归一化。在参考数据集中操纵时间的机会是一个独特的特性，它使rcVPC能够直观地表征具有延迟效应发作的暴露-反应关系。在一系列受实际数据启发的示例中，将rcVPC方法与pcVPC和传统VPC进行了比较。结果表明，rcVPC方法能够以VPC或pcVPC图无法实现的方式，为模型开发提供更直观的解释和更有效的指导。
