Knowing for the sake of knowing: the value of prognostic information.
In evaluating diagnostic tests, traditional methods in decision analysis often emphasize how the results of the test will or will not affect patient management. Clinicians are advised to avoid testing if the results will not alter treatment strategy or other management plans. But patients may be interested in the prognostic information that testing provides even if it is not used to guide treatment. The authors present a model that defines this prognostic information as the expected deviation from the prior probability of disease. The model generates utility functions that are curvilinear over prior probabilities. Whereas the traditional threshold approach to medical decision making produces at most three zones of management strategy (withhold, test, and treat), the incorporation of prognostic information into threshold analysis produces two additional zones (test but withhold anyway, and test but treat anyway). Conditions under which one or both of these additional zones will appear are described. The model justifies the practice of performing tests that cannot alter management plans; it explains the unwillingness of some patients to undergo diagnostic testing when they fear unwanted results; and it provides a method for quantifying the sensitive nature of confidential tests. The model is illustrated using the antibody test for the Smith antigen. This test has a high specificity but a low sensitivity for lupus erythematosus. Clinicians may use the test because a positive result will support their prior suspicion of disease even though they may not change their management strategy if the test result is negative. The advantage of testing in this setting lies in the test's potential for establishing with virtual certainty that the disease is present. Thus, the test is valued for the prognostic information it provides apart from its effect on patient management.
Asch
D A
DA
Robert Wood Johnson Foundation Clinical Scholars Program, University of Pennsylvania School of Medicine, Philadelphia 19104-4283.
Patton
J P
JP
Hershey
J C
JC
D003663:Decision Trees; D007257:Information Theory; D008962:Models, Theoretical; D010361:Patients / Q000523:psychology*; D011237:Predictive Value of Tests; D011379:Prognosis; D013997:Time Factors