Medical diagnostic research is prone to bias and even more importantly to yielding information that is not useful to patients or physicians and sometimes overstates the value of diagnostics. Problems include conditioning on the wrong statistical information, reversing the flow of time, and categorization of inherently continuous test outputs and disease severity. Sensitivity, specificity, and ROC curves are highly problematic. So is categorical thinking.

The many advantages of diagnostic risk modeling will be discussed, and this talk will show how pre- and post-test diagnostic models give rise to clinically useful displays that quantify diagnostic utility in a way that is useful to patients, physicians, and diagnostic device makers. And unlike sensitivity and specificity, post-test probabilities are immune to certain biases, including workup bias.