The document discusses the validity of screening tests, focusing on the definitions, objectives, and key measures such as sensitivity, specificity, and predictive values. It explains the importance of distinguishing between true positives and false positives/negatives, the implications of cut-off points, and the utility of Receiver Operating Characteristic (ROC) curves in optimizing test performance. Additionally, it covers the method of combining tests to enhance diagnostic accuracy and the influence of disease prevalence on predictive values.

1. Validity Of A Screening Test
Dr. Kulrajat Bhasin
1

2. • Definitions
• Objective
• Sensitivity & Specificity
• Concept of false positives and false negatives
• Tests for continuous variables
• Combination of tests and effects
• Predictive value of a test
• Determining cut-off point
• ROC curve 2
Plan Of Presentation

3. Definition
• The process by which unrecognized disease or defects are
identified by means of rapidly applied tests, examination or
other procedures in apparently healthy individuals on a
large scale.
• Differs from periodic health exam as:
i. Capable of wide application
ii. Relatively inexpensive
iii.Physician has to only interpret it and hence saves time.
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4. Aims And Objectives
• To sort out from a large group of apparently
healthy people, those likely to have disease or
are at an increased risk.
• To bring those who are apparently abnormal
under supervision, confirmation and treatment if
required.
4

5. Three Key Measures Of Validity
1. SENSITIVITY
2. SPECIFICITY
3. PREDICTIVE VALUE
5

6. • The above figure is a bimodal distribution of a
variable in the normal and diseased populations.
• Below is a fig representing unimodal distribution.
6

7. 7

8. Validity Of Screening Tests
• The validity of a screening test is defined as its ability to
distinguish between who has a disease and who does not and
has 2 components:
 Sensitivity : defined as ability of a test to identify correctly
those who have the disease or true positives.
 Specificity : defined as the ability of a test to identify correctly
those who do not have a disease or true negatives.
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9. 9
Screening
test results
Diagnosis
Total
Present Absent
Positive a (True positive) b (False positive) a + b
Negative c (False negative) d (True negative) c + d
Total a + c b + d a+b+c+d

10. Sensitivity
It is defined as the ability of a test to identify correctly
all those who have the disease, that is "true positive".
x 100 x100
• A test has 90% sensitivity means: 90 per cent of the
diseased people screened by the test will give a "true
positive" result and the remaining 10 per cent a "false
negative" result.
(TP)
(TP+FN)
(a)
(a + c)
OR
10

11. Specificity
It is defined as the ability of a test to identify correctly those who do not
have the disease, that is, "true negatives".
x 100 x 100
• 90 per cent specificity means: 90 per cent of the non diseased
persons will give "true negative" result and 10 per cent of non-
diseased people screened by the test will be wrongly classified as
"diseased" when they are not (False positives).
(TN)
(FP+TN)
OR
(d)
(b + d)
11

12. 12

13. Percentage Of False Positives:
x
• Means that patients who do not have the disease
are told that they have the disease.
• Further diagnostic test.
• Inconvenience, discomfort, anxiety & unnecessary
expense.
(b)
(b + d)
100
13

14. Percentage Of False Negatives:
x
• Means that patients who actually have the disease are
told that they do not have the disease.
• False Re-assurance.
• Might ignore the development of signs & symptoms.
• Postpone treatment.
• IMPORTANT IN SERIOUS DISEASE.
(c)
(a + c)
100
14

15. • At times we need to test for a continuous variable,
e.g. BP or blood sugar level for which there is no
“positive” or “negative result”.
• A decision must therefore be made in establishing a
cut-off level above which a test result is considered
positive and below which a result is considered
negative.
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Tests For Continuous Variables

16. 16
3 2
7 8
7 5
3 5
DIABETIC
DIABETIC DIABETICNON-DIABETIC
NON-DIABETIC
NON-DIABETIC
HIGH
HIGH
HIGH
DIABETIC
DIABETIC NON-DIABETIC
NON-DIABETIC
BLOOD
SUGAR BLOOD
SUGAR
BLOOD
SUGAR
LOW
LOW
LOW
10 10
10 10
+
+
-
-
Screening For Diabetes In Hypothetical Population With A Prevalence Of 50 %.
Effects Of Choosing Different Cutoff Levels For A Positive Test:
A B
C

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HIGH
HIGH
HIGH
BLOOD
SUGAR
BLOOD
SUGAR
BLOOD
SUGAR
LOW LOW
LOW
HIGH
BLOOD
SUGAR
LOW
D E
F G

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19. 19

20. Combination Of Tests
• Two or more tests can be used in combination to
enhance the sensitivity or specificity of screening.
E.g. for syphilis, pts. first evaluated by RPR test,
whose sensitivity is high but still gives false
positives, hence the second test is applied, e.g.
FTA – ABS which is a more specific test and the
resultant positives are true positives.
• Sequential (two stage) and simultaneous testing.
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21. • We can calculate the net sensitivity and specificity by using both tests in sequence.
• After finishing both tests 315 people of 500 actual diabetics in the 10,000 population
have been correctly called positive, hence 63 % net sensitivity (loss).
• 7600 of 9500 in this population who never had DM were correctly called negative by
the first test and were not tested further, while an addl 1710 of those 9500 were
correctly called negative by 2nd
test hence 7600 + 1710/9500 x 100, i.e. 98 % net
specificity (gain).
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Sequential (Two Stage) Testing

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Simultaneous Testing
Total population: 1000 ; prevalence: 20%

23. …In Summary
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• In sequential testing, there is a net loss in sensitivity but a
net gain in specificity compared with results by either test
alone.
• In simultaneous testing, there is a net gain in sensitivity but
a net loss in specificity compared with either tests done
alone.
• Hence the decision to use either of the above methods
depends upon whether they’re being done for screening or
diagnosis as well as on considerations of practicality, i.e.
setting in which testing is being done, costs, length of
hospital stay, degree of invasiveness, as well as third party
insurance coverage.

24. Predictive Values Of A Test :
• Useful to know what proportion of patients with abnormal test
results are truly abnormal.
• They reflect the diagnostic power of a test.
Positive Predictive Value:
The "predictive value of a positive test" indicates the probability that
a patient with a positive test result has, in fact, the disease in
question.
• Proportion of patients with positive test results who are correctly
diagnosed.
• PPV of 90% means that 90% of the patients who are diagnosed to be
positive by the test in fact have the disease in question.
x
(a)
(a + b)
100
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25. Negative Predictive Value:
The "predictive value of a negative test" indicates the probability that a
patient with a negative test result doesn't have disease in question.
• NPV of 90% means that 90% of the patients who are diagnosed to be
negative by the test do not have the disease in question.
x
(d)
(c + d)
100
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26. 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
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27. 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
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28. Effect of Prevalence :
• Predictive values depend strongly on prevalence of the
condition.
• As the prevalence of the condition increases positive
predictive value increases and thus more chances of getting
true positive results.
• If the condition is uncommon it is more sure that the
negative test indicates no abnormality.
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29. • Higher the prevalence, higher is the predictive value.
• Hence a screening program is most productive and
efficient when it is directed to high risk target
population. 29
Relationship B/W Predictive Value And Disease
Prevalence

30. Amount of previous unrecognized disease diagnosed as a
result of screening effort.
•Depends on
Sensitivity, specificity.
Prevalence of disease.
Participation of individuals.
• High risk populations selected for screening – 40 yrs.
above adults for diabetes.
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Yield

31. Determining The Cutoff Point
The factors to be considered are :
• Disease prevalence : when prevalence of the disease is
high in the community the cut-off point is set at low
level.
This will increase the sensitivity.
• The disease : if the disease is very lethal and early
intervention markedly increases the prognosis, cut off
point is set at lower level.
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32. Receiver Operating Characteristic (ROC) Curve
• The cutoff point for a disease is best determined using ROC curve.
• As no single value cutoff point of an individual test can be expected to have
both perfect sensitivity and specificity, it is often necessary to determine
which value or cut off point is most appropriate for a given purpose.
• Receiver Operating Characteristic (ROC) Curve is a graph displaying the
relationship between true positive rate (on vertical axis) and false positive
rate (on horizontal axis).
• The ROC curve was first used during world war 2 for the analysis of RADAR
signals for the detection of enemy objects entering the battle field.
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33. • A perfect test can be represented by drawing a line along the Y-axis
and horizontal line on the top.
• ROC curve of most of the tests appear as a curved line. Better the test,
closer the curve approaches depiction of perfect test.
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Uses Of ROC Curve:
1. ROC curve helps to choose the critical cut-off value which best discriminates
the presence or absence of a disease.
- At which the curve’s deviation is maximum.
-Where the perpendicular distance from the line equality is maximum.

35. 35
Test A Test B
Uses Of ROC Curve:
2. Another use of ROC curve is to compare two indicators.
The curve that contains a large area below it is a better predictor than
one below with a smaller area.

36. References
• Park K. Park’s textbook of preventive and social medicine.
22nd
ed. Jabalpur(India): Banarsidas Bhanot; 2012. p127-134.
• Leon Gordis, Text book of epidemiology.4th
ed.Saunders:
Elsevier Inc.; 2009.p.85-108
• Sukon Kanchanaraksa.Evaluation of diagnostic and screening
tests: validity and reliability, 1st
edition. John hopkins
bloomberg school of public health. 2011. p 1 – 124.
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