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The model prediction was evaluated using a visual predictive check (VPC), which evaluates whether the identified model would be able to predict the observed variability for 80% of the population in the PK data that were used for model identification [ 28].
The verification of model prediction was conducted through leave-one-out cross-validation (CV).
Model prediction was possible independent from loading, spray rate and drum rotation speed.
The model prediction was in good agreement with observed data (R2 = 0.969).
The relative median error of the model prediction was in general less than 10%.
RESULTS: Among 998,651 patients, in the best-performing model, prediction was strong for patients in the highest trajectory group (C-statistic: 0.86; R: 0.47).
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In doing so, a closed-loop model prediction is performed.
Finally, the model prediction is better for low carbon atoms.
The model prediction is thus only partially correct.
For the submultiplicative model, prediction is improved with respect to the multiplicative model while, for the supra-multiplicative model, prediction is worse.
A natural question for the model prediction is whether this model estimation is reasonable.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com