title: Bayesian ROC curve estimation under verification bias.
abstract: Receiver operating characteristic (ROC) curve has been widely used in medical science for its ability to measure the accuracy of diagnostic tests under the gold standard. However, in a complicated medical practice, a gold standard test can be invasive, expensive, and its result may not always be available for all the subjects under study. Thus, a gold standard test is implemented only when it is necessary and possible. This leads to the so-called 'verification bias', meaning that subjects with verified disease status (also called label) are not selected in a completely random fashion. In this paper, we propose a new Bayesian approach for estimating an ROC curve based on continuous data following the popular semiparametric binormal model in the presence of verification bias. By using a rank-based likelihood, and following Gibbs sampling techniques, we compute the posterior distribution of the binormal parameters intercept and slope, as well as the area under the curve by imputing the missing labels within Markov Chain Monte-Carlo iterations. Consistency of the resulting posterior under mild conditions is also established. We compare the new method with other comparable methods and conclude that our estimator performs well in terms of accuracy.
authors.affiliation: Duke Clinical Research Institute, Duke University Medical Center, PO Box 17969, Durham, NC 27715, U.S.A.
authors.forename: Jiezhun
authors.initials: J
authors.lastname: Gu
authors.forename: Subhashis
authors.initials: S
authors.lastname: Ghosal
authors.forename: David E
authors.initials: DE
authors.lastname: Kleiner
mesh_terms: D001499:Bayes Theorem; D015982:Bias; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D018579:Patient Selection; D012372:ROC Curve; D012044:Regression Analysis
keywords: MAR assumption; ROC curve; binormal model; posterior consistency; verification bias-correction