Figure 4l.

Comparison of prediction performance

We then fitted the model to a reduced number of cycles (1-5) and predicted the respective next cycle toxicity. We also used the individual parameters estimated by the regression models of clinical risk factors to predict toxicity of cycles without utilizing any platelet data under therapy. Results of our outcome measures {MDDr,k} and {SDDr,k} 1≤r<k≤6 for the mechanistic and semi-mechanistic models are displayed in Figure 5. The mechanistic model shows clearly better predictive power than the semi-mechanistic model for predicting cycles 2-6. The predictive performance of the parameters obtained from regression analysis of clinical factors are comparable for the two models. Prediction performance of the semi-mechanistic model improves only slightly when adding the cycle data. In contrast, the prediction performance of the mechanistic model clearly improves when adding cycle information, especially after adding at least one cycle. The same applies when considering LDD rather than the DD as primary measure of prediction performance (see Figure 6).