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Bayesian ROC curve estimation under verification bias
Author(s) -
Gu Jiezhun,
Ghosal Subhashis,
Kleiner David E.
Publication year - 2014
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6297
Subject(s) - gold standard (test) , receiver operating characteristic , estimator , markov chain monte carlo , posterior probability , bayesian probability , consistency (knowledge bases) , statistics , gibbs sampling , computer science , prior probability , mathematics , artificial intelligence
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. Copyright © 2014 John Wiley & Sons, Ltd.