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Sensitivity and specificity are prevalence-independent test characteristics, as their values are intrinsic to the test and do not depend on the disease prevalence in the population of interest. [6] Positive and negative predictive values , but not sensitivity or specificity, are values influenced by the prevalence of disease in the population ...
They use the sensitivity and specificity of the test to determine whether a test result usefully changes the probability that a condition (such as a disease state) exists. The first description of the use of likelihood ratios for decision rules was made at a symposium on information theory in 1954. [ 1 ]
The log diagnostic odds ratio can also be used to study the trade-off between sensitivity and specificity [5] [6] by expressing the log diagnostic odds ratio in terms of the logit of the true positive rate (sensitivity) and false positive rate (1 − specificity), and by additionally constructing a measure, :
The relationship between sensitivity and specificity, as well as the performance of the classifier, can be visualized and studied using the Receiver Operating Characteristic (ROC) curve. In theory, sensitivity and specificity are independent in the sense that it is possible to achieve 100% in both (such as in the red/blue ball example given above).
For example, the ACR criteria for systemic lupus erythematosus defines the diagnosis as presence of at least 4 out of 11 findings, each of which can be regarded as a target value of a test with its own sensitivity and specificity. In this case, there has been evaluation of the tests for these target parameters when used in combination in regard ...
Specificity will generally be higher than sensitivity, especially when people have COVID-19 symptoms—in other words, false-negative COVID-19 tests are more likely than false positives.
Youden's J statistic is = + = + with the two right-hand quantities being sensitivity and specificity.Thus the expanded formula is: = + + + The index was suggested by W. J. Youden in 1950 [1] as a way of summarising the performance of a diagnostic test; however, the formula was earlier published in Science by C. S. Pierce in 1884. [2]
For example, in this sense, an MRI is the gold standard for brain tumor diagnosis, though it is not as good as a biopsy. In this case, the sensitivity and specificity of the gold standard are not 100% and it is said to be an "imperfect gold standard" or "alloyed gold standard". [12]