Figure 2.

Overview of the workflow in model‐informed precision dosing comparing full Bayesian inference to maximum a posteriori (MAP)‐based prediction. In full Bayesian inference, uncertainties in the parameter values are propagated to uncertainties in the observable space and quantities of interest. The posterior $p(\mathrm{\theta }|y_{1:n})$ is displayed for the parameters “Slope” (drug effect parameter) and “Circ0” (pretreatment neutrophil concentration). For the prior and full Bayes (reference) approach (sampling importance resampling with $S={10}^6$) samples (dots) from the distributions are shown with contour levels. In the observable space, the point estimates (solid lines) are displayed with the central 90% confidence interval or credible intervals (dashed lines and shaded area) along with the therapeutic drug/biomarker monitoring data (crosses). The a priori/a posteriori probabilities are calculated for the neutropenia grades (grade 0–4). Note that $y_1$ corresponds to the measurement of baseline neutrophil counts (“Circ0”) and is taken into account in the posterior.