Bayesian Nonparametric Youden Index Modeling

• Activity Number: 74
• Type: Contributed
• Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Miguel de Carvalho*+ and Vanda Inacio and Adam Branscum
• Companies: Pontificia Universidad Católica de Chile and Pontificia Universidad Católica de Chile and Oregon State University
• Keywords: Bayesian nonparametrics ; diagnostic testing ; Dirichlet process mixtures ; Markov chain Monte Carlo ; Youden index

Abstract

Accurate diagnosis of disease is of crucial importance in medical research.The accuracy of the test at any given threshold can be measured by the probability of a true positive (sensitivity) and a true negative (specificity).The receiver operating characteristic (ROC) curve plots the sensitivity, Se(c), against 1-specificity, 1-Sp(c), as the threshold c varies through the range of possible test results. To evaluate the discriminatory ability of a test it is common to summarize the information of the ROC curve into a single global value or index. In this work we focus on one of such indices, the Youden index (YI), which can be defined as YI = max_c{Se(c) + Sp(c)-1} and has the attractive feature of providing a criterion for choosing the optimal threshold value, c^{*}, to screen subjects in practice. With the aim of having a flexible model that can handle skewness, multimodality and other nonstandard features of the data, we propose to estimate the Youden index and its associated optimal threshold c^{*} using Dirichlet process mixtures. The performance of the estimator is evaluated through a simulation study and a real data application is provided.