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Editorial
. 2010 Jun;149(6):878-881.e2.
doi: 10.1016/j.ajo.2010.01.001.

Diagnostic tests: understanding results, assessing utility, and predicting performance

Alain B Labrique  1 , William K-Y Pan
Affiliations
Editorial

Diagnostic tests: understanding results, assessing utility, and predicting performance

Alain B Labrique et al. Am J Ophthalmol. 2010 Jun.
No abstract available

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Figures

FIGURE 1
FIGURE 1
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.
FIGURE 2
FIGURE 2
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.
FIGURE 3
FIGURE 3
Graph depicting the effect of disease prevalence on positive predictive value at 3 levels of test sensitivity (specificity held constant at 90%).
See this image and copyright information in PMC

References

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