| Publication Abstract Display |
| Type: Published Manuscript |
| Title: Quantifying relative superiority among many binary-valued diagnostic tests in the presence of a gold standard. |
| Authors: Deutsch R, Mindt MR, Xu R, Cherner M, Grant I, and the HNRC Group |
| Year: 2009 |
| Publication: Journal of Data Science |
| Volume: 7 Issue: Pages: 161-177 |
| Abstract:Comparison of more than two diagnostic or screening tests for prediction of presence vs. absence of a disease or condition can be complicated when attempting to simultaneously optimize a pair of competing criteria such as sensitivity and specificity. A technique for quantifying relative superiority of a diagnostic test when a gold standard exists in this setting is described. The proposed {it superiority index} is used to quantify and rank performance of diagnostic tests and combinations of tests. Development of a validated model containing a subset of the tests may be improved by eliminating tests having a very small value for this index. To illustrate, we present an example using a large battery of neuropsychological tests for prediction of cognitive impairment. Using the proposed index, the battery is reduced with favorable results. |