A Regression Approach to Visual Predictive Checks for Population Pharmacometric Models

Abstract

A visual predictive check (VPC) is a common diagnostic procedure for population pharmacometric models. Typically, VPCs are generated by specifying intervals, or “bins”, of an independent variable (e.g., time). However, bin specification is not always straightforward and the choice of bins may affect the appearance, and possibly conclusions, of VPCs. The objective of this work was to demonstrate how regression techniques can be used to derive VPCs and prediction-corrected VPCs (pcVPCs) for population pharmacometric models. This alternative approach negates the need for empirical bin selection. The proposed method utilizes local and additive quantile regression. Implementation is straightforward and computationally acceptable. This work provides support for deriving VPCs and pcVPCs via regression techniques.