multiple diagnostic tools are used by emergency physicians, every day. in addition, new tools are evaluated to obtain more accurate methods and reduce time or cost of conventional ones. in the previous parts of this educational series, we described diagnostic performance characteristics of diagnostic tests including sensitivity, specificity, positive and negative predictive values, and likeliho...

Abstract The development of medical diagnostic tests is of great importance in clinical practice, public health, and medical research. The receiver operating characteristic (ROC) curve is a popular tool for evaluating the accuracy of such tests. We review Bayesian nonparametric methods based on Dirichlet process mixtures and the Bayesian bootstrap for ROC curve estimation and regression. The me...

Receiver operating characteristic (ROC) curve is widely applied in measuring discriminatory ability of diagnostic or prognostic tests. This makes ROC analysis one of the most active research areas in medical statistics. Many parametric and semiparametric estimation methods have been proposed for estimating the ROC curve and its functionals. In this paper, we propose a fully nonparametric Bayesi...

For many years now, there is a growing interest around ROC curve for characterizing machine learning performances. This is particularly due to the fact that in real-world problems misclassification costs are not known and thus, ROC curve and related metrics such as the Area Under ROC curve (AUC) can be a more meaningful performance measures. In this paper, we propose a quadratic programming bas...

The accuracy of a medical diagnostic tool depends on its specificity, the probability that it classifies a normal person as normal, and its sensitivity, the probability that it classifies a diseased person as diseased. The receiver operating characteristic (ROC) curve of such a tool is its sensitivity plotted against (1 specificity) as the threshold defining “normal” versus “diseased” ranges ov...

Receiver operating characteristic (ROC) curves and in particular the area under the curve (AUC), are widely used to examine the effectiveness of diagnostic markers. Diagnostic markers and their corresponding ROC curves can be strongly influenced by covariate variables. When several diagnostic markers are available, they can be combined by a best linear combination such that the area under the R...

Receiver Operating Characteristics (ROC) curve serves as a statistical tool to measure the quality of Biomarker in 5 accessing the accuracy of any diagnostic test. This paper portrays the historical background of ROC curve and reviews various 6 parametric methods adopted for fitting the ROC curve till date. The parametric ROC models that are considered in this paper 7 are Bi-Normal, Bi-Gamma, B...

The receiver operating characteristic (ROC) curve is used for classification between two populations. Usually two independent random samples from the populations with absolutely continuous cumulative distribution functions (CDF) F and G are considered. Then the ROC curve is defined as ROC(t) = 1−G(F−1(1− t)) for 0 < t < 1 if the inverse F−1 exists. Classical ROC curve estimator is based on empi...

Receiver operating characteristic (ROC) curve is widely applied in measuring discriminatory ability of diagnostic or prognostic tests. This makes the ROC analysis one of the most active research areas in medical statistics. Many parametric and semiparametric estimation methods have been proposed for estimating the ROC curve and its functionals. In this paper, we propose the Bayesian bootstrap (...