Diagnostic Tests: Understanding Results, Assessing Utility, and Predicting Performance

An evaluation of a new diagnostic test, compared with a known gold standard assessment. There are 241 individuals in this population known to be ill (a + c), and 113 who are considered normal (b + d). Using this 2 × 2 table approach, we can assess the sensitivity, specificity, and predictive strengths of the new test, as described.

Graph showing the consequences of cutoff selection for a continuous diagnostic test, such as antibody levels or low-density lipoprotein (LDL) cholesterol values. Depending on what cutoff value (α) is chosen, a subject may be identified correctly as sick (“True Positives”) or healthy (“True Negatives”), and a certain rate of ascertainment error can be expected (“False Positives,” “False Negatives”). In this example, a cutoff has been chosen to minimize the rate of false negatives, or individuals incorrectly labeled as healthy, at the expense of a larger number of false positives. This would be appropriate for conditions for which mislabeling and treating someone as sick is less egregious than missing truly sick individuals.

Graph depicting the effect of disease prevalence on positive predictive value at 3 levels of test sensitivity (specificity held constant at 90%).