|
Although
sensitivity is sometimes used to mean "responsiveness"
(the degree to which a scaling instrument detects clinically important
changes) it is more common for "sensitivity and specificity"
to be used in tandem to describe the accuracy or utility of a
diagnostic test. Sensitivity refers to the probability of a positive
test among patients who are identified as having the disease;
specificity refers to the probability of a negative test among
patients who are correctly put at ease as not having the disease.
A sensitive test, when negative, rules out disease; a specific
test, when positive, rules in disease. Measures of sensitivity
[a/(a+c)] and specificity [d/(b+d)] are derived as ratios based
on numbers of patients that fall into the following "2 x
2" table:
| Table
22.1: Sensitivity and Specificity Tests
|
|---|
| | Patients
with Disease
| Patients
without Disease
|
|---|
| Test
is Positive
| a | b |
|---|
|
Test
is Negative
| c | d |
|---|
|
Question
22.1
In most
cases, as sensitivity increases, specificity decreases and vice
versa.
 | True |
 | False |
Positive and negative
predictive values may be more useful to clinicians than sensitivity
and specificity. The positive predictive value of a test [a/(a+b)]
refers to the probability of disease among patients with a positive
test. The negative predictive value of a test [d/(c+d)] refers
to the probability of no disease among patients with a negative
test.
|