Intermediate, indeterminate, and uninterpretable diagnostic test results.
Diagnostic tests do not always yield positive or negative results; sometimes the results are intermediate, indeterminate, or uninterpretable. No consensus exists for the incorporation of such results into data assessment. Conventional Bayesian analysis leads investigators to either exclude patients with non-positive, non-negative results from their studies or categorize such results into inappropriate cells of the standard four-cell decision matrix. The authors propose a standardized method for reporting results in studies dealing with diagnostic test use and discuss how researchers should expand the four-cell matrix to six cells when non-positive, non-negative results occur. They suggest that the six-cell matrix with new operational definitions of sensitivity, specificity, likelihood ratios, and test yield should be adopted routinely. In addition, they define the different types of non-positive, non-negative results and demonstrate how clinicians can use tree-structured decision analysis from the six-cell matrix. While their method does not solve all problems posed by non-positive, non-negative results, it does suggest a standard method for reporting these results and utilizing all the data in decision making.
D001499:Bayes Theorem; D019411:Clinical Laboratory Techniques; D003657:Decision Making; D006801:Humans; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D011336:Probability