3. BIOSTATISTICS
1. How is the sensitivity of a test defined? What are highly sensitive tests used for clinically?
Sensitivity is defined as the ability of a test to detect disease—mathematically, the number
of true positives divided by the number of people with the disease. Tests with high sensitivity are
used for disease screening. False positives occur, but the test does not miss many people with the
disease (low false-negative rate).
2. How is the specificity of a test defined? What are highly specific tests used for clinically?
Specificity is defined as the ability of a test to detect health (or nondisease)—mathematically,
the number of true negatives divided by the number of people without the disease. Tests with high
specificity are used for disease confirmation. False negatives occur, but the test does not call anyone
sick who is actually healthy (low false-positive rate). The ideal confirmatory test must have high
sensitivity and high specificity; otherwise, people with the disease may be called healthy.
3. Explain the concept of a trade-off between sensitivity and specificity.
The trade-off between sensitivity and specificity is a classic statistics question. For example,
you should understand how changing the cut-off glucose value in screening for diabetes (or chang-
ing the value of any of several screening tests) will change the number of true- and false-negative
as well as true- and false-positive results. If the cut-off glucose value is raised, fewer people will
be called diabetic (more false negatives, fewer false positives), whereas if the cut-off glucose value
is lowered, more people will be called diabetic (fewer false negatives, more false positives).
If the cut-off serum glucose value for a
Normal Diabetic diagnosis of diabetes mellitus is set at
subjects patients point A, no cases of diabetes will be
missed, but many people without dia-
betes will be mislabeled as diabetics (i.e.,
higher sensitivity, lower specificity,
lower positive predictive value, higher
negative predictive value). If the cut-off
is set at B, the diagnosis of diabetes will
not be made in healthy people, but many
cases of true diabetes will go undiag-
nosed (i.e., lower sensitivity, higher
A B
specificity, higher positive predictive
value, lower negative predictive value).
Glucose levels The optimal diagnostic value lies some-
where between points A and B.
4. Define positive predictive value (PPV). On what does it depend?
When a test is positive for disease, the PPV measures how likely it is that the patient has the
disease (probability of having a condition, given a positive test). PPV is calculated mathemati-
cally by dividing the number of true positives by the total number of people with a positive test.
PPV depends on the prevalence of a disease (the higher the prevalence, the higher the PPV) and
the sensitivity and specificity of the test (e.g., an overly sensitive test that gives more false posi-
tives has a lower PPV).
5. Define negative predictive value (NPV). On what does it depend?
When a test comes back negative for disease, the NPV measures how likely it is that the
patient is healthy and does not have the disease (probability of not having a condition, given a
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