186 PART 4 Comparing Groups

on the context of the condition, to optimize the accuracy and get the best of both

worlds while minimizing both false positive and false negative results.

In a screening test with a sensitivity of 100 percent, the test result is always posi-

tive whenever the condition is truly present. In other words, the test will identify

all individuals who truly have the condition. When a perfectly sensitive test comes

out negative, you can be sure the person doesn’t have the condition. You calculate

sensitivity by dividing the number of true positive cases by the total number of

cases where the condition was truly present: a/c1 (that is, true positive/all

present). Using the data in Figure 13-4, sensitivity

/

33 37, which is 0.89. This

means that the home test comes out positive in only 89 percent of truly pregnant

women, and the other 11 percent were really pregnant, but had a false positive

result in the test.

A perfectly specific test never produces a false positive result for an individual

without the condition. In a test that has a specificity of 100 percent, whenever the

condition is truly absent, the test always has a negative result. In other words, the

test will identify all individuals who truly do not have the condition. When a per-

fectly specific test comes out positive, you can be sure the person has the condi-

tion. You calculate specificity by dividing the number of true negative cases by the

total number of cases where the condition was truly absent: d

c

/ 2 (that is, true

negative/all not present). Using the data in Figure 13-4, specificity

/

51 63, which

is 0.81. This means that among the women who were not pregnant, the home test

was negative only 81 percent of the time, and 11 percent of women who were truly

negative tested as positive. (You can see why it is important to do studies like this

before promoting the use of a particular screening test!)

But imagine you work in a lab that processes the results of screening tests, and

you do not usually have access to the gold standard results. You may ask the ques-

tion, “How likely is a particular screening test result to be correct, regardless of

whether it is positive or negative?” When asking this about positive test results,

you are asking about positive predictive value (PPV), and when asking about neg-

ative test results, you are asking about negative predictive value (NPV). These are

covered in the following sections.

Sensitivity and specificity are important characteristics of the test itself. Observe

that the answers depend on the prevalence of the condition in the background

population. If the study population were older women, then the prevalence of

being pregnant would be lower, and that would impact the sensitivity and speci-

ficity. The prevalence will also impact the PPV and NPV, which we discuss in the

next section. For these reasons, it is important to use natural sampling in such a

study design.