Validity refers to how accurately a screening test measures a disease. Key measures of validity include sensitivity, specificity, and predictive value. Sensitivity measures the percentage of true positives, specificity measures the percentage of true negatives, and predictive value refers to the probability that the test result correctly identifies whether someone has the disease or not. The prevalence of a disease in a population also affects the predictive power of screening tests. Combining multiple screening tests can increase overall sensitivity and specificity for more accurate disease detection.

1. Validity and
Screening test
results
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Roll.89
Guided by Dr. Sipra mam

2. Validity
Refers to what extent the test accurately
measures the disease
• Expresses the ability of a test to distinguish
those have the disease from those who don’t
• Eg: for diabetes, screening test is urine
glucose examination but more valid test is
glucose tolerance test

3. Three key measures of validity
1. Sensitivity
2. Specificity
3. Predictive value

4. Screening tests result
includes..
• True positives
• True
negatives
• False
positives
• False
• True positives
• True
negatives
• False
positives
• False

5. 1. True positives - sick people
correctly diagnosed as sick
2. False positives - healthy
people incorrectly identified as sick
3. True negatives - healthy people
correctly identified as healthy
4. False negatives - sick people
incorrectly identified as healthy

6. Screening test result
Test
result
disease
d
Not
diseased
Total
Positiv
e
A (true
positive)
B (false
positive)
A+B
Negativ
e
C (false
negative)
D (true
negative)
C+D
Total A+C B+D
A+B+C+D

7. Sensitivity - ability of a test to identify
correctly all those who have a disease i.e.
true positives.
• If the test is highly sensitive & the test result
is negative, you can certain that they don’t
have disease
• 90% sensitivity means 90% of diseased are
screened as true positives & remaining 10%
are true negatives.
Sensitivity =A/A+C x 100

8. Specificity - ability of the test to identify
those who don’t have the disease correctly as
true negatives
•If the test is highly specific & test result is
positive it means they actually have a disease
•If 90% of specificity means 90% of people
are true negatives & remaining 10% will be
wrongly diagnosed as diseased
Specificity =D/B+D x 100

9. • 2/> tests can be used in combination in order to
increase the sensitivity and specificity of a test
• E.g. For syphilis, patients first evaluated by RPR
test, whose sensitivity is high but still gives false
positives, hence second test is applied, i.e. FTA-
ABS which is more specific test and the resultant
positives are true positives.
• Types - 1. Sequential testing
Combinational testing

10. Comparison
• Performed separately.
• both times we are eliminating
negative results (FN,TN)
which are indicators of actual
positives.
• Indicator of sensitivity.
net sensitivity is low. net
specificity is high
• Performed in parallel.
• Positive result in any one
of the tests, is considered
as positive in disease.
• Net sensitivity is higher..
But specificity is low
Simultaneous testingSequential testing

11. Test
results 1900
150
7600
2250
7750
315 190
35 1710
505
1745
Test
results
Sequential testing
Test-1 (Blood sugar)- Diabetes
Test-2 (GTT)- Diabetes
350

12. Simultaneous testing

13. 144
200 people having disease
180 test positive by test B
but some of them are tested positive by
both of them
160 TP by
test A
180 TP
by test B
16 TP only
by test A
36 TP only
by test B
160 TP by test A
144 TP by both tests
16 36
Thus net sensitivity using both tests
simultaneously
16 +144 +36 / 200 = 198/ 200 = 98%
Net sensitivity

14. 800 people who do not have disease
480 TN by test A
720 TN by test B
Some of them are tested negative by
both tests
480 TN by test A
720 TN
by test B
48 TN only by
test A 288 TN only by
test B
432 TN by tests
A & B
Thus , net specificity using both
tests simultaneously
432
432 / 800 = 54 %
Net specificity

15. Determined by predictive value
a. positive predictive value
b. negative predictive value
• Useful to know what proportion of patients with
abnormal tests results are truly abnormal.
• They reflect diagnostic power of a test
Predictive accuracy

16. The predictive value of a positive test
indicates the probability that a patient with a
positive test result has the disease in question.
• PPV of 90% means 90% of people who are
diagnosed to be positive by the test in fact have
the disease in question.
calculated by
(A) X 100
(A + B )
Positive predictive value

17. The predictive value of a negative test
indicates the probability that a patient with a negative
test result doesn’t the disease in question.
•NPV of 90% means 90% of patients who are
diagnosed to be negative by the test in fact do not
have the disease .
(D) X 100
(C +D)
Negative predictive value

18. • A test is used in 50 people with disease
and 50 people without..
48. 3 51
2 47 49
Disease
Test

19. • Sensitivity = 48/50 = 96%
• Specificity = 47/50 = 94%
• Positive predictive value = 48/51 = 94%
• Negative predictive value = 47/49 = 96%

20. Predictive values depend strongly on
prevalence of the condition.
• As the prevalence of a condition increases
PPV increases ,thus more chances of
getting TP results.
• If the condition is uncommon , then a
negative test indicates no abnormality.
Effect of prevalence

21. Relationship of disease prevalence to positive
predictive value
Eg: sensitivity=99%,specificity=95%
Disease
prevalence
Test values Sick Not sick Totals PPV
1%
5%
+
_
Totals
+
_
Totals
99
1
100
495
5
500
495
9,405
9,900
475
9,025
9,500
594
9,406
10,000
970
9,030
10,000
99/594 =17%
495/970 =51%

22. Thank you