Sensitivity (tests)

Editor-In-Chief: C. Michael Gibson, M.S., M.D. [1]; Assistant Editor(s)-In-Chief: Kristin Feeney, B.S.

Overview[edit | edit source]

Sensitivity refers to the statistical measure of how well a binary classification test correctly identifies a condition^[1]. In epidemiology, this is referred to as medical screening tests that detect preclinical disease. In quality control, this is referred to as a recall rate, whereby factories decided if a new product is at an acceptable level to be mass-produced and sold for distribution.

Critical Considerations[edit | edit source]

• The results of the screening test are compared to some absolute (Gold standard); for example, for a medical test to determine if a person has a certain disease, the sensitivity to the disease is the probability that if the person has the disease, the test will be positive.
• The sensitivity is the proportion of true positives of all diseased cases in the population. It is a parameter of the test.
• High sensitivity is required when early diagnosis and treatment is beneficial, and when the disease is infectious.

Worked Example[edit | edit source]

Template:SensSpecPPVNPV

Definition[edit | edit source]

<math>{\rm sensitivity}=\frac{\rm number\ of\ True\ Positives}{{\rm number\ of\ True\ Positives}+{\rm number\ of\ False\ Negatives}}.</math>

A sensitivity of 100% means that the test recognizes all sick people as such.

Sensitivity alone does not tell us how well the test predicts other classes (that is, about the negative cases). In the binary classification, as illustrated above, this is the corresponding specificity test, or equivalently, the sensitivity for the other classes.

Sensitivity is not the same as the positive predictive value (ratio of true positives to combined true and false positives), which is as much a statement about the proportion of actual positives in the population being tested as it is about the test.

The calculation of sensitivity does not take into account indeterminate test results. If a test cannot be repeated, the options are to exclude indeterminate samples from analyses (but the number of exclusions should be stated when quoting sensitivity), or, alternatively, indeterminate samples can be treated as false negatives (which gives the worst-case value for sensitivity and may therefore underestimate it).

SPPIN and SNNOUT[edit | edit source]

Terminology in Information Retrieval[edit | edit source]

In information retrieval, positive predictive value is called precision, and sensitivity is called recall.

F-measure: can be used as a single measure of performance of the test. The F-measure is the harmonic mean of precision and recall:

<math>F = 2 \times ({\rm precision} \times {\rm recall}) / ({\rm precision} + {\rm recall}).</math>

In the traditional language of statistical hypothesis testing, the sensitivity of a test is called the statistical power of the test, although the word power in that context has a more general usage that is not applicable in the present context. A sensitive test will have fewer Type II errors.

Related Chapters[edit | edit source]

• binary classification
• receiver operating characteristic
• specificity (tests)
• statistical significance
• Type I and type II errors
• Selectivity

Online Calculators[edit | edit source]

• MedCalc's Sensitivity/Specificity Calculator

References[edit | edit source]

External links[edit | edit source]

• Sensitivity and Specificity Medical University of South Carolina

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