A simple decision analytic solution to the comparison of two binary diagnostic tests Journal Article


Authors: Vickers, A. J.; Cronin, A. M.; Gonen, M.
Article Title: A simple decision analytic solution to the comparison of two binary diagnostic tests
Abstract: One of the most basic biostatistical problems is the comparison of two binary diagnostic tests. Commonly, one test will have greater sensitivity, and the other greater specificity. In this case, the choice of the optimal test generally requires a qualitative judgment as to whether gains in sensitivity are offset by losses in specificity. Here, we propose a simple decision analytic solution in which sensitivity and specificity are weighted by an intuitive parameter, the threshold probability of disease at which a patient will opt for treatment. This gives a net benefit that can be used to determine which of two diagnostic tests will give better clinical results at a given threshold probability and whether either is superior to the strategy of assuming that all or no patients have disease. We derive a simple formula for the relative diagnostic value, which is the difference in sensitivities of two tests divided by the difference in the specificities. We show that multiplying relative diagnostic value by the odds at the prevalence gives the odds of the threshold probability below which the more sensitive test is preferable and above which the more specific test should be chosen. The methodology is easily extended to incorporate combinations of tests and the risk or side effects of a test. © 2012 John Wiley & Sons, Ltd.
Keywords: prostate cancer; decision analysis; molecular markers; diagnostic tests; combination tests; sequential testing
Journal Title: Statistics in Medicine
Volume: 32
Issue: 11
ISSN: 0277-6715
Publisher: John Wiley & Sons  
Date Published: 2013-05-20
Start Page: 1865
End Page: 1876
Language: English
DOI: 10.1002/sim.5601
PROVIDER: scopus
PMCID: PMC3531575
PUBMED: 22975863
DOI/URL:
Notes: --- - Cited By (since 1996):1 - "Export Date: 3 June 2013" - "CODEN: SMEDD" - "Source: Scopus"
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  1. Mithat Gonen
    1028 Gonen
  2. Andrew J Vickers
    880 Vickers
  3. Angel M Cronin
    145 Cronin