Receiver operating characteristic (ROC) curves are often used to evaluate predictive accuracy of statistical prediction models. We present other measures which not only incorporate the statistical but also the clinical consequences of using a particular prediction model. Depending on the disease and population under study, the misclassification costs of false positives and false negatives vary. The concept of Decision Curve Analysis (DCA) takes this cost into account, by using a threshold probability (the probability above which a patient opts for a particular treatment). Using the DCA technique, a Net Benefit Curve is built by plotting “Net Benefit", which is a function of the expected benefit and expected harm using a model, by the corresponding threshold probability. We use the threshold probability range that is relevant to the disease and the population under study is then used to plot the net benefit curve to obtain the optimum results using a statistical model.
We present the process of construction of a summary measure to find which predictive model yields the highest net benefit. One intuitive approach is to calculate the area under the net benefit curve. We examined the
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