Equation 3
\begin{equation}
\text{MADPE}_{i}(\%)=median\ \{|\text{PE}_{\text{ij}}|,\ j=1,\ldots,\ N_{i}\}\nonumber \\
\end{equation}To go further in the evaluation of these models, the predictive
performance of Bayesian forecasting was assessed according to Broeckeret al. (12). Calculations of MDPE and MDAPE enable the comparison
of different model-predicted and observed concentrations.
Fractionated databases were created to assess the impact of observed
tobramycin concentrations on the predictive performance: (a) complete
database, (b) removal of all observed peaks, (c) removal of all
residuals, (d) removal of all dosages after Day3, (e) removal of all
observed concentration (a priori prediction). (c) and (d) were
applied only for patients with more than 2 doses.
An MDPE between –20% and 20% and an MDAPE ≤ 30% are considered
acceptable criteria for bias and inaccuracy (13).
The predictive performance was further evaluated by simulating 500
tobramycin concentrations per time point using model PK parameters as
fix and the $SIM of NONMEM®. To assess the forecasting error between
the observed and the simulated dataset, the 5th, 50th and 95th
percentile of the simulated data over the time dose (Predicted Corrected
Visual Predictive Check [pcVPC]) was plotted. With the same
simulated dataset, the distribution of the Normalized Prediction
Distribution Error (NDPE) was also plotted. Normal distribution of NPDEs
were checked visually by using the histogram of NPDEs frequency.