A Bayesian approach to a general regression model for ROC curves.
A fully Bayesian approach to a general nonlinear ordinal regression model for ROC-curve analysis is presented. Samples from the marginal posterior distributions of the model parameters are obtained by a Markov-chain Monte Carlo (MCMC) technique--Gibbs sampling. These samples facilitate the calculation of point estimates and credible regions as well as inferences for the associated areas under the ROC curves. The analysis of an example using freely available software shows that the use of noninformative vague prior distributions for all model parameters yields posterior summary statistics very similar to the conventional maximum-likelihood estimates. Clinically important advantages of this Bayesian approach are: the possible inclusion of prior knowledge and beliefs into the ROC analysis (via the prior distributions), the possible calculation of the posterior predictive distribution of a future patient outcome, and the potential to address questions such as: "What is the probability that a certain diagnostic test is better in one setting than in another?"
Hellmich
M
M
Department of Epidemiology and Public Health, University of Leicester, UK.
Abrams
K R
KR
Jones
D R
DR
Lambert
P C
PC
D001499:Bayes Theorem; D003661:Decision Support Techniques; D006801:Humans; D016013:Likelihood Functions; D008113:Liver Neoplasms / Q000000981:diagnostic imaging / Q000556:secondary; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D012372:ROC Curve; D012044:Regression Analysis; D014463:Ultrasonography