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.
Peng
F
F
Department of Mathematics and Statistics, University of Nebraska, Lincoln 68588-0323, USA.
Hall
W J
WJ
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