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Med Decis Making. 1996 Oct-Dec;16(4):404-11.

Bayesian analysis of ROC curves using Markov-chain Monte Carlo methods.

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Department of Mathematics and Statistics, University of Nebraska, Lincoln 68588-0323, USA.


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.

[Indexed for MEDLINE]

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