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