validity and screening test

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