# Sensitivity (tests)

 Articles WikiDoc Resources for Sensitivity (tests)

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

## Overview

Sensitivity refers to the statistical measure of how well a binary classification test correctly identifies a condition. 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

• 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.

## Definition

${\rm {sensitivity}}={\frac {\rm {number\ of\ True\ Positives}}{{\rm {number\ of\ True\ Positives}}+{\rm {number\ of\ False\ Negatives}}}}.$ 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

SPPIN SNNOUT Neither Near-perfect
Proposed definition Sp > 95% SN > 95% Both < 95% Both > 99%
Example Many physical dx findings Ottawa fracture rules Exercise treadmill test HIV-1/HIV-2 4th gen test
Predictive values:
10% pretest prob PPV= 35%

NPV = 99%

PPV = 64%

NPV = 98%

PPV = 31%

NPV = 97%

PPV = 92%

NPV > 99%

50% pretest prob PPV = 94%

NPV = 83%

PPV = 83%

NPV = 94%

PPV = 80%

NPV = 80%

PPV = 99%

NPV = 99%

90% pretest prob PPV = 98%

NPV = 64%

PPV = 99%

NPV = 35%

PPV = 97%

NPV = 31%

PPV > 99%

NPV = 92%

Clinical messages Accept test result when:
2. maybe when pretest was a toss-up
Accept test result when:
1. confirms a strong suspicion
Accept test result unless:
Notes:

Green font indicates when results are more likely to be trustable
Red font indicates SPPIN/SNNOUT errors when you should be suspicous a a SPPIN/SNNOUT result

## Terminology in Information Retrieval

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:

$F=2\times ({\rm {precision}}\times {\rm {recall}})/({\rm {precision}}+{\rm {recall}}).$ 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.