Any test of the stability of the conclusions of a health care evaluation over a range of probability estimates, value judgments, and assumptions about the structure of the decisions to be made. This may involve the repeated evaluation of a decision model in which one or more of the parameters of interest are varied.
A figure depicting the power of a diagnostic test. The ROC curve presents the test’s true-positive rate (i.e., sensitivity) on the horizontal axis and the false-positive rate (i.e., 1 – specificity) on the vertical axis for different cut-points dividing a positive from a negative test. An ROC curve for a perfect test has an area under the curve = 1.0 while a test that performs no better than chance has an area under the curve of only 0.5.