Bayesian analysis of ROC curves using Markov-chain Monte Carlo methods.
The authors introduce a Bayesian approach to generalized linear regression models for rating data observed in the evaluation of a diagnostic technology. Such models were previously studied using a non-Bayesian approach. In a Bayesian analysis, the difficulties inherent in an ordinal rating scale are circumvented by using data-augmentation techniques. Posterior distributions for the regression parameters- and thereby for receiver operating characteristic (ROC) curve parameters and values, for the area under a ROC curve, differences between areas, etc.-may then be computed by Markov-chain Monte Carlo methods. Inferences are made in standard Bayesian ways. The methods are exemplified by a study of ultrasonography rating data for the detection of hepatic metastases in patients with colon or breast cancer (previously analyzed) and the results compared.
Department of Mathematics and Statistics, University of Nebraska, Lincoln 68588-0323, USA.
F
F
Peng
W J
WJ
Hall
D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D003110:Colonic Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D003952:Diagnostic Imaging / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D016014:Linear Models; D008113:Liver Neoplasms / Q000000981:diagnostic imaging / Q000556:secondary; D008390:Markov Chains; D009010:Monte Carlo Method; D012372:ROC Curve; D014463:Ultrasonography