Overview of Inference about Roc Curve in Medical Diagnosis

Roc Curve Estimation: an Overview

• This work overviews some developments on the estimation of the Receiver Operating Characteristic (ROC) curve. Estimation methods in this area are constantly being developed, adjusted and extended, and it is thus impossible to cover all topics and areas of application in a single paper. Here, we focus on some frequentist and Bayesian methods which have been mostly employed in the medical setti...

Bayesian nonparametric approaches for ROC curve inference

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...

Empirical likelihood inference for the area under the ROC curve.

For a continuous-scale diagnostic test, the most commonly used summary index of the receiver operating characteristic curve (ROC) is the area under the curve (AUC) that measures the accuracy of the diagnostic test. In this article, we propose an empirical likelihood (EL) approach for the inference on the AUC. First we define an EL ratio for the AUC and show that its limiting distribution is a s...

ROC Curve

It would be nice for both the woman and family and the attending obstetrician to anticipate a cesarean delivery on the basis of patient characteristics. The traditional method is to compute Bishop score based on dilatation, effacement, consistency and position. Other parameters that influence the success of induction of labor are maternal age, parity, BMI and amniotic fluid index. A study was c...

Roc Curve Analysis and Medical Decision Making: What’s the Evidence That Matters for Evidence-based Diagnosis?