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# Measures of diagnostic accuracy

Diagnostic accuracy measures the ability of a test to detect a condition when it is present and detect the absence of a condition when it is absent.

Comparison of the result of a diagnostic test to the true known condition of each subject classifies each outcome as:

True positive (TP) | Test result correctly identifies the presence of the condition. |

False positive (FP) | Test result incorrectly identifies the presence of the condition when it was absent. |

True negative (TN) | Test result correctly identifies the absence of the condition. |

False negative (FN) | Test result incorrectly identifies the absence of the condition when it was present. |

A perfect diagnostic test can discriminate all subjects with and without the condition and results in no false positive or false negatives. However, this is rarely achievable, as misdiagnosis of some subjects is inevitable. Measures of diagnostic accuracy quantify the discriminative ability of a test.

**Sensitivity / Specificity**

Sensitivity and specificity are the probability of a correct test result in subjects with and without a condition respectively.**Likelihood ratios**

Likelihood ratios are the ratio of the probability of a specific test result for subjects with the condition, against the probability of the same test result for subjects without the condition.**Predictive values**

Predictive values are the probability of correctly identifying a subject's condition given the test result.**Youden J**

Youden's J is the likelihood of a positive test result in subjects with the condition versus those without the condition.

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- Administrator's Guide
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- Distribution
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- Item reliability
- Fit model
- Method comparison
- Measurement systems analysis (MSA)
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- Diagnostic performance
- Measures of diagnostic accuracy
- Sensitivity / Specificity
- Likelihood ratios
- Predictive values
- Youden J
- Estimating sensitivity and specificity of a binary test
- Comparing the accuracy of two binary diagnostic tests
- ROC plot
- Plotting a single ROC curve
- Comparing two or more ROC curves
- Area under curve (AUC)
- Testing the area under a curve
- Difference in area under curve (AUC)
- Testing the difference between the area under two curves
- Decision thresholds
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Version 5.30

Published 15-Apr-2019