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The measure for predictive performance was calculated as the mean prediction error of the individual predicted minus observed HbA1c at end-of-trial for each arm.
Nested cross-validation determined the best number of biomarkers to retain and evaluate for predictive performance.
Therefore the need for predictive performance models is increasing but model development has generally lagged behind the introduction of new coatings.
The use of relevance may be a practical approach because only covariates important for predictive performance are included.
We have proposed to use the overall discrimination index C introduced by Harrel and other authors [ 21- 24] as the measure for predictive performance of a categorized variable.
However, concerns have been raised that this degree of correlation is not sufficient since correlation is not a good measure for predictive performance [ 5].
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For evaluation of predictive performance, data up to 12 weeks were applied for prediction of HbA1c at end-of-trial.
For evaluation of predictive performance, we evaluated the ability of the model to predict mean HbA1c at end-of-trial (Table 3 ) for subjects with an HbA1c assessment at end-of-trial.
Figure 3 shows pc-VPCs for evaluation of predictive performance of the bridged KPD model in the 64 warfarin-treated children.
The simulated fibrinogen concentrations from the lumped model were compared with those from the original model at each time point to assess for loss of predictive performance.
The trials used for evaluation of predictive performance consisted of three phase III trials and one phase IV trial of previously approved pharmaceuticals.
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