Bayes and diagnostic testing.
Interpretation of the result of a diagnostic test depends not only on the actual test result(s) but also on information external to this result, namely the test's sensitivity and specificity. This external information (also called prior information) must be combined with the data to yield the so-called updated, posterior estimates of the true prevalence and the test characteristics. The Bayesian approach offers a natural, intuitive framework in which to carry out this estimation process. The influence of the prior information on the final result may not be ignored. Guidance for the choice of prior information not in conflict with the data can be obtained from a set of statistics and indices (DIC, p(D), Bayes-p).
Lesaffre
Emmanuel
E
Biostatistical Centre, Catholic University of Leuven, Leuven, Belgium.
Speybroeck
Niko
N
Berkvens
Dirk
D
D000820:Animal Diseases / Q000175:diagnosis*; D000818:Animals; D001499:Bayes Theorem; D003955:Diagnostic Tests, Routine / Q000662:veterinary*; D008954:Models, Biological; D012680:Sensitivity and Specificity