Fig. 8
Visual Predictive Check - Corrected Prediction. This figure provides an intuitive assessment of misspecification in structural, variability, and covariate models. The aim is to assess graphically whether simulations from a model of interest are able to reproduce both the central trend and variability in the observed data, when plotted versus an independent variable (typically time). It summarizes the structural and statistical models by computing several quantiles of the empirical distribution of the data after having regrouped them into bins over successive intervals. VPCs can be misleading if applied to data that include a large variability in dose and/or influential covariates, or that follow adaptive designs such as dose adjustments. The prediction-corrected VPC (pcVPC), with prediction correction, was developed to maintain the diagnosis value of a VPC in these cases. In each bin, the observed and simulated data are normalized based on the typical population prediction for the median time in the bin. This removes the variability coming from binning across independent variables.
