Background. Several medical articles discuss methods of constructing confid
ence intervals for single proportions and the likelihood ratio, but scant a
ttention has been given to the systematic study of intervals for the poster
ior odds, or the positive predictive value, of a test. Methods. The authors
describe 5 methods of constructing confidence intervals for posttest proba
bilities when estimates of sensitivity, specificity and the pretest probabi
lity of a disorder are derived from empirical data. They then evaluate each
method to determine how well the intervals' coverage properties correspond
to their nominal value. Results. When the estimates of pretest probabiliti
es, sensitivity, and specificity are derived from more than 80 subjects and
are not close to 0 or 1, all methods generate intervals with appropriate c
overage properties. When these conditions are not met, however, the best pe
rforming method is an objective Bayesian approach implemented by a simple s
imulation using a spreadsheet. Conclusion. Physicians and investigators can
generate accurate confidence intervals for posttest probabilities in small
-sample situations using the objective Bayesian approach.