# ​​​Accuracy: Diagnostic Sensitivity and Specificity

The following calculations are used to communicate to end users the accuracy of a qualitative test method. Qualitative test methods are those that give a positive or negative diagnosis, regardless of whether the assay produces results with numerical values.

## Definitions of Performance Characteristics

KEY

TP = True Positive
FP = False Positive
TN = True Negative
FN = False Negative

Diagnostic Sensitivity

Percentage of positive samples correctly identified in an experiment with known positive controls; used to assess risk of false negative results.

#### TP / (TP + FN)

Diagnostic Specificity

Percentage of negative samples correctly identified in an experiment with known negative controls; used to assess risk of false positive results.

#### TN / (TN + FP)

Global Accuracy

Percentage of all correct diagnoses in an experiment; accounts for both false positive and false negative results

## Calculati​ng Performance Characteristics of a Single Test

Data Input

Total number of samples in experiment = ____ (number entered would be used to generate rows in table below)

## Reporting and Interpreting Results

Results are reported as percentages. A percentage greater than 50% indicates that the test is a better predictor of sample diagnosis than ​random chance.

Where possible, it is recommended to design a validation experiment to include the test method under study and a standard method. In this way the accuracy values can be compared between tests to describe how they differ. These differences can be used to inform their intended use and/or place in a larger diagnostic protocol.

Example:

• Test method A has a Diagnostic Sensitivity of 85% and a Diagnostic Specificity of 95%.
• Test method B has Diagnostic Sensitivity of 95% and a Diagnostic Specificity of 85%.

Test B is less specific (greater risk false positive) but more sensitive (low risk false negative) compared to Test A.
Test B could be used as a screening tool and Test A used as the confirmatory method in a diagnostic protocol.

## Comparing Performance Characteristics of Multiple Tests

The diagnostic sensitivities and specificities of two tests can be compared graphically using Receiving Operator Characteristic (ROC) curves. A ROC curve plots diagnostic sensitivity by the false positive rate (1 – diagnostic specificity).

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• A curve that is shifted right has a higher false positive rate.
• A curve with a lower maximum amplitude is less sensitive.
• A curve that is a diagonal line would represent a test whose accuracy is the same as random chance.

The area under the ROC Curve (AUROCC or concordance statistic) also informs a user about a test’s accuracy. The AUROCC value gives an estimation of the likelihood that a test method will give a correct diagnosis.

• If the AUROCC is 1, the test is a perfect predictive tool of infected status.
• If the AUROCC is 0.5 or less, the test has no predictive power.

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