Fig 2 Test and treatment thresholds. What clinicians expect of a good test is that results change the probability sufficiently to confirm or exclude a diagnosis. Tests, however, are altering only the probability of a disease of interest being present. If a test result moves the probability of the condition of interest to below the test threshold, this indicates that the condition is very unlikely, the downsides associated with any further testing and treatment for this condition outweigh any anticipated benefit, and no further testing or treatment for that condition should follow. If the test result increases the probability of disease to above the treatment threshold, this indicates that the condition is very likely, confirmatory testing that raises the probability of the condition further is unnecessary, and the anticipated benefits of treatment outweigh potential harms. If the pre-test probability is above the treatment threshold, further confirmatory testing that raises the probability further would not be helpful. If the pre-test probability is below the test threshold, further exclusionary testing would not be useful. When the probability is between the test and treatment thresholds, testing will be useful. Test results are of greatest value when they shift the probability across either threshold

Fig 1 Two generic ways in which a test or diagnostic strategy can be evaluated. On the left, patients are randomised to a new test or strategy or to an old test or strategy. Those with a positive test result (cases detected) are randomised (or were previously randomised) to receive the best available management (second step of randomisation for management not shown). Investigators evaluate and compare patient-important outcomes in all patients in both groups.2 On the right, patients receive both a new test and a reference test (old or comparator test or strategy). Investigators can then calculate the accuracy of the test compared with the reference test (first step). To make judgments about importance to patients of this information, patients with a positive test (or strategy) in either group are (or have been in previous studies) submitted to treatment or no treatment; investigators then evaluate and compare patient-important outcomes in all patients in both groups (second step)

Fig 3 Example of heterogeneity in diagnostic test results. Sensitivity and specificity of multislice coronary computed tomography compared with coronary angiogram (from Hamon et al4). This heterogeneity also existed for likelihood ratios and diagnostic odds ratios