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Validity and Screening test
results
P. AnnapurnaRoll.89
Guided by Dr. Sipra mam
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
Three key measures of validity1. Sensitivity2. Specificity3. Predictive value
Screening tests result includes..
• True positives• True negatives• False positives• False
negatives
1. True positives - sick people correctly diagnosed as sick
2. False positives - healthy people incorrectly identified as
sick3. True negatives - healthy people
correctly identified as healthy
4. False negatives - sick people incorrectly identified as healthy
Screening test result Test result
diseased
Not diseased
Total
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
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
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
• 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 2. Simultaneous testing
Combinational testing
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 testing
Sequential testing
Test results 1900
150
7600
2250
7750
315 190
35 1710
505
1745
Test results
Sequential testingTest-1 (Blood sugar)- Diabetes
Test-2 (GTT)- Diabetes
350
Simultaneous testing
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
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
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
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
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
• A test is used in 50 people with disease and 50 people without..
48. 3 51
2 47 49
Disease
Test
• Sensitivity = 48/50 = 96%
• Specificity = 47/50 = 94%
• Positive predictive value = 48/51 = 94%
• Negative predictive value = 47/49 = 96%
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
Relationship of disease prevalence to positive predictive value
Eg: sensitivity=99%,specificity=95%
Disease prevalence Test
valuesSick Not sick Totals PPV
1%
5%
+_
Totals
+_
Totals
991
100
4955
500
4959,405
9,900
4759,025
9,500
5949,406
10,000
9709,030
10,000
99/594 =17%
495/970 =51%
Thank you
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