EditorialThe Wizard of Odds: Bayes' Theorem and Diagnostic Testing
Section snippets
How Should We Quantify the Diagnostic Accuracy of a Particular Test Procedure?
Predictive Accuracy.—When symptomatic patients with abnormal electrocardiographic stress test responses first came to be referred for coronary angiography, the proportion with disease—the predictive accuracy of an abnormal test response—was noted to be very high. But when the identical testing procedure was later extended to asymptomatic subjects, the proportion with disease was surprisingly low. This puzzling paradox was eventually resolved through Bayes' theorem, which states that the
How Should We Interpret a Particular Test Response in an Individual Patient?
Result-Specific Measures.—As noted earlier, sensitivity and specificity (and their associated likelihood ratios) are usually expressed in terms of categorical thresholds (all test responses beyond some putative cutoff being considered equivalently abnormal). These thresholds have no effect on our assessment of the test's discriminant accuracy (in terms of ROC area, for example), but they are wholly unsuited to the matter of clinical interpretation. Individual patients have a particular
Health Care implications
Arcane as they may be, quantitative measures of diagnostic testing have practical relevance to all those who provide health care resource utilization, technology assessment, and patient management, as well as to those who must ultimately judge the quality of such efforts—journal editors, granting agencies, third-party payers, and practitioners. The following examples illustrate some potential applications.
Resource Utilization.—There is some range of prior probability of disease (P) within which
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