This document discusses diagnostic testing and key terms related to test accuracy. It defines sensitivity as the ability of a test to correctly identify those with a condition, and specificity as the ability to correctly identify those without a condition. Sensitivity answers what percentage of sick people a test identifies, while specificity answers what percentage of well people a test identifies as negative. Predictive values depend on disease prevalence in the population and indicate the likelihood a positive or negative test result is correct. High sensitivity means fewer false negatives, while high specificity means fewer false positives.

1. Testing, Testing, 1…2…3:
The Basics of Diagnostic
Testing
Brian N. Eisen
The Eisen Law Firm, Co., L.P.A.
1300 E. 9th St., Suite 1801
Cleveland, OH 44114
(216) 687-0900
brian@eisenlawfirm.com

2. Diagnostic Tests are
Important
But are They Worth a Damn?

3. Our Population

4. The Perfect Diagnostic Test

5. Diagnostic Tests are NOT Perfect

6. Three Key Terms
1. SENSITIVITY
2. SPECIFICITY
3. PREDICTIVE VALUE

7. SENSITIVITY
Sensitivity is a measure of how good a test
is at correctly identifying those individuals
with the condition or disease.
It is defined as the fraction of the diseased
who test positive.
Sensitivity answers the question, “If we
tested only sick people, what percentage of
them would the test correctly identify”?

8. Sensitivity: Look ONLY at those
with the Condition

9. If our test is 80%
Sensitive:
If our test is 100%
Sensitive:
All 20 subjects WITH THE
CONDITION will test
positive!
16 of 20 subjects WITH
THE CONDITION will test
positive.

10. Real Life Example #1
High Sensitivity
Most Home Pregnancy Tests
Claim to be >95% Sensitive

11. Real Life Example #2
Low Sensitivity
H1N1 Rapid Test Only 40% Sensitive

12. So, for Every 10 People
with the Flu…

13. The Test Only Gets 4 Correct!

14. # Positive Tests (4) + # Negative Tests (6) = 1
All with flu (10) All with flu (10)
SENSITIVITY + FALSE NEGATIVE RATE = 1
Sensitivity and False Negatives are
Complementary

15. Here’s Another Way to Look at it:
Those who
test + are
“True
Positives”
Those who
test - are
“False
Negatives”
Sensitivity = TP/(TP +FN) X 100

16. Sensitivity and False Negatives are
Complementary
• Sensitivity + False Negative Rate = 1
• A test with 40% Sensitivity has a 60% False
Negative Rate
• A test with 75% Sensitivity has _____ %
False Negatives
25

17. Q: How Can you Develop a Test
that is 100% Sensitive?
A: Make it Always Positive!

18. Sensitivity: So What?
• Sensitivity + False Negative Rate =1
• The greater the Sensitivity, the Fewer False
Negatives
• So, a Very Sensitive Test will have Very Few
False Negatives
• LESSON: IF YOU GET A NEGATIVE, IT’S
PROBABLY A TRUE NEGATIVE!
• RULE: SnOUT (SENSITIVE, NEGATIVE,
OUT)

19. SPECIFICITY
Specificity is a measure of how good a test is
at correctly identifying those individuals who
do not have the disease.
It is defined as the fraction of the non-
diseased who test negative.
Specificity answers the question, “If we
tested only those who are well (do not have
the condition or disease), what percentage
would the test correctly identify”?

20. Specificity: Look ONLY at those
WITHOUT the Condition

21. If our test is 100%
Specific:
All 80 subjects WITHOUT
THE CONDITION will test
negative!
If our test is 75%
Specific:
60 of 80 subjects WITHOUT
THE CONDITION will test
negative.

22. Here’s Another Way to Look at it:
Those who
test + are
“False
Positives”
Those who
test - are
“True
Negatives”
Specificity = TN/(TN +FP) X 100

23. Specificity and False Positives are
Complementary
• Specificity + False Positive Rate = 1
• A test with 40% Specificity has a 60% False
Positive Rate
• A test with 75% Specificity has _____ % False
Positives
25

24. Specificity: So What?
• Specificity + False Positives =1
• The greater the Specificity, the Fewer False
Positives
• So, a Very Specific Test will have Very Few
False Positives
• LESSON: IF YOU GET A POSITIVE, IT’S
PROBABLY A TRUE POSITIVE!
• RULE: SpIN (SPECIFIC, POSITIVE, IN)

25. Real Life Example #1
High Specificity
WHAT DOES THIS MEAN?

26. 27% Sensitive
• If you test a hospitalized inpatient with a
UTI, only 27% chance it will be positive.
• 73% of hospitalized patients with UTI will
have a false negative test.
• Not very good to rule out UTI.

27. 94% Specific
• If you test a hospitalized inpatient without a
UTI, there is a 94% chance the test will be
negative.
• Only a few (6%) of patients without UTI
will have a falsely positive test.
• Good test to rule in UTI.
SpIN

28. Real Life Example #2:
Low Specificity
WHAT DOES THIS MEAN?

29. 100% Sensitive
• If someone has mediastinitis, his CT will be
positive always.
• No one will have a falsely negative CT (no
one with mediastinitis will have a negative
CT).
• If CT is negative, you can rule out
mediastinitis.
SnOUT

30. 33% Specific
• Only 1/3 people without mediastinitis will
have a negative CT.
• 2/3 people will have a positive CT, even
though they don’t have mediastinitis.
• Not a good test to rule in mediastinitis.

31. In Practice
• When a doctor tells you she ruled out a
condition with a negative result on a
particular test, find out its sensitivity
(SnOUT). If it’s not very high, go after her.
• When a doctor tells you she ruled in a
condition with a positive result on a
particular test, find out its specificity
(SpIN). If it’s not very high, go after her.

32. Questions Answered
Sensitivity answers the question, “If
we tested only sick people, what
percentage of them would the test
correctly identify”?
Specificity answers the question, “If we
tested only those who are well (do not
have the condition or disease), what
percentage would the test correctly
identify”?

33. Population  Individual
• In the clinical setting, a more important
question is:
• If the test results are positive (or
negative) in a given patient, what is
the probability that this patient has
(or does not have) the disease?
• In other words:
What proportion of patients who test
positive (or negative) actually have (or do
not have) the disease in question?

34. PREDICTIVE VALUE
Positive predictive value is the
proportion of all people with positive
tests who have the disease.
Negative predictive value is the
proportion of all people with negative
tests who do not have the disease.

35. PPV: Chance a Positive Result is
Correct
In this example, 20 out of 100 positive
results are correct. PPV = 20%

36. NPV: Chance Negative Result is
Correct
In this example, 80 out of 100 negative
results are correct. NPV = 80%

37. CRITICAL THING TO NOTE ABOUT
PREDICTIVE VALUES:
Unlike Sensitivity and Specificity,
PREDICTIVE VALUES DEPEND
UPON THE PREVALENCE OF A
DISEASE IN THE POPULATION.

38. THE HIGHER THE PREVALENCE,
THE HIGHER THE POSITIVE PREDICTIVE VALUE
When almost everyone has it, the chance
of a positive test being right is high!

39. THE LOWER THE PREVALENCE,
THE HIGHER THE NEGATIVE PREDICTIVE VALUE
When almost no one has it, the chance of
a negative test being right is high!

40. In Practice
• A doctor tells you he ordered an invasive
treatment because the patient tested positive
in a test known to have a “high PPV.”
• Q: Is this a sound decision?
• A: It depends.
– Is the patient from the population where the
PPV was determined?
If the patient comes from a population with a much
lower prevalence, PPV would be much lower, and it
might be risky to attempt the treatment!

41. If the Published PPV comes
from this population:

42. You don’t want to apply it to a
member of this population:

43. CALCULATING THE RATES
A test is used in 50 people with disease
and 50 people without. These are the
results:
Disease
+ -
Test
+ 48 3 51
- 2 47 49
50 50 100

44. Sensitivity = 48/50 = 96%
Specificity = 47/50 = 94%
Positive predictive value = 48/51 = 94%
Negative predictive value = 47/49 = 96%
Disease
+ -
Test
+ 48 3 51
- 2 47 49
50 50 100

45. Now, let’s take this test out into a
population where 2% of people have the
disease, not 50% as in the previous
example. Assume there are 10,000 people,
and the same sensitivity and specificity as
before, namely 96% and 94%, respectively
Disease
+ -
Test
+ 192 588 780
- 8 9,212 9,220
200 9,800 10,000

46. • What is the positive predictive value
now?
192/780 = 24.6%
• When the prevalence of disease is 50%,
94% of positive tests indicate disease. But
when prevalence is only 2% -- less than
one in four -- positive test results indicate
a person with disease, and 2% actually
would represents a very common disease.

47. Brian’s Cheat Sheet