for paired data. However, it can also analyze balanced unpaired data where the number of units is the same for each classifier. It cannot be used with partially-paired data. 2) The outcome distribution. Depending on this factor, three types of approaches can be more or less appropriate for fitting ROC curves and estimating the corresponding AUCs: (i) A parametric approach [10,11], where we assume a parametric distribution for the outcomes of the positive and negative individuals. (ii) A semiparametric approach, where we assume that discrete ordinal outcomes correspond to classification of an unobserved latent decision variable into ordinal categories defined by unknown cut-points or threshold values, or that continuous outcomes can be expressed as an unknown monotonic transformation of the latent distribution [12], with positive and negative individuals having different latent decision variables. In this case, parametric distributions (e.g. normal, logistic, log-normal) are assumed for the latent decision variables. (iii) A non-parametric approach [13,14], where no distributional assumptions are made about the outcomes for the positive and negative individuals.
