Bayesian Nonparametric Approaches for ROC Curve Inference

Calhau Fernandes, Inacio De Carvalho Vanda; Jara, Alejandro; Bras De Carvalho, Miguel

Bayesian Nonparametric Approaches for ROC Curve Inference

Date

2015

Authors

Calhau Fernandes, Inacio De Carvalho Vanda

Jara, Alejandro

Bras De Carvalho, Miguel

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 methods are illustrated by means of data concerning diagnosis of lung cancer in women.

Citation

Vanda Inácio de Carvalho, Alejandro Jara, Miguel de Carvalho. Bayesian Nonparametric Approaches for ROC Curve Inference. In: Riten Mitra and Peter Mueller,editors. Nonparametric Bayesian Inference in Biostatistics. Springer; 2015. p. 327-344.