For each type of approach, different methods for estimating the AUC after the ROC curve is generated have been described [11,15,16]. The advantages and disadvantages of using one or another approach under different scenarios have been previously assessed [17,18]. Among the advantages of using a parametric or semiparametric method are that these methods generate a smooth ROC curve, and the assumption of a distribution provides a natural means by which statistical inference such as hypothesis testing and confidence intervals can be achieved. When data deviate from the assumed distribution (e.g. normal or log-normal) or simply the outcome distribution for the positive or negative individuals is uncertain, non-parametric methods for estimating the ROC curve become a useful and robust alternative. Even though the ROC curve generated by non-parametric methods is jagged, that problem has been tackled in a non-parametric manner by means of kernel density estimation of the empirical distributions in a previous study [13].
