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Dynamic prediction of response using longitudinal profi les 97
to use in clinical practice and to extend the logistic regression with the dynamic
model fit can easily be incorporated. No extra statistical computational work has to
be done, but inserting the new observed markers in the estimated prediction model.
For this reason the method has a strong clinical applicability.
To compare the predictive performance of the different approaches the aspects of
discrimination and calibration are studied. 11 The c-statistics for each week and for
each method is plotted in figure 5 together with the results of the baseline model
using only PI. Past week 16 an increase of the c-statistics is observed, suggesting
that beyond baseline prediction at least 16 weeks of treatment is necessary before
an early update of prediction of response is sensible. The best discriminative ability
for this data was observed with the direct approaches. The calibration slope11 for
each week are depicted in figure 6. The indirect methods all have suffers from
severe calibration problems. The predictors behave more like classifiers and are
overconfident. The direct methods perform well with a calibration slope close to
one, with a little overfitting for the direct approach with the observed markers mainly
before week 16.
Figure 5. The AUC (c-statistics) for the different methods by visit week.