The Reference-Corrected Visual Predictive Check: A More Intuitive Diagnostic for Non-Linear Mixed Effects Models

MIDD

Pharmacometrics

Methodology

AAPS J. 2025 Apr 29;27(4):86.

Ibrahim MMA, Jonsson EN, Bergstrand M

About the publication

The prediction-corrected visual predictive check (pcVPC) is a commonly used model diagnostic, especially in the context of heterogeneous study designs or adaptive dosing. While useful, the transformation applied in prediction correction can result in plots that are unintuitive and difficult to interpret — even for experienced modelers.

This publication presents the reference-corrected visual predictive check as an alternative approach. Developed by Martin Bergstrand, Niclas Jonsson, and Moustafa M. A. Ibrahim (Previously Pharmetheus, currently Alexion, AstraZeneca Rare Disease), rcVPC addresses key limitations of pcVPCs by leveraging a user-defined reference set of independent variables to normalize observed and simulated data. This normalization supports clearer y-axis interpretation and enhances the communicability of results.

A distinguishing feature of rcVPC is the ability to manipulate the time variable in the reference dataset, enabling visualization of steady state exposure–response relationships in the presence of delayed effects — a challenge for standard VPC and pcVPC methods.

In this publication, the methodology is illustrated through examples inspired by real case-studies with comparisons to traditional VPCs and pcVPCs. The results highlight how rcVPC can improve the interpretability of model diagnostics and support more effective communication during model development.

About the publication

Acknowledgements