Validity

The validity of a diagnostic test result, depends on the measures employed before, during, and after each assay. Consistency in the production of good results requires a standardized operating procedure that includes quality assurance, quality control, and quality assessment (1).

Accuracy

The accuracy (performance) of a screening test is expressed in four dimensions (sensitivity, specificity, positive predictive value and negative predictive value), The prevalence of the disease or condition tested for affects some - but not all - of the test performance characteristics (1).  

 

 Does the person truly have the condition?

 

YES

NO

Test result

 Positive

 A (true  positive)

 B (false positive)

 A + B

 Negative

 C (false negative)

 D (true negative

 C + D

 

 A + C

 B + D

 A + B + C + D

The sensitivity of a diagnostic test measures the proportion of those people who have the disease who are correctly detected by the test (test positive).  The sensitivity of a test can only be measured among patients for whom the diagnosis is already confirmed by other means than the test we study.

The specificity of a diagnostic test is the proportion of those people who do not have the disease who are correctly left undetected by the test (test negative). The specificity of a test can only be measured among patients for who are already confirmed not to have the disease or condition tested for.

Predictive values

We perform a diagnostic test because we do not know the diagnosis. The real questions to be answered when performing a diagnostic test are: "What proportion of the patients tested as positive really have the disease?"and "What proportion of the patients the tested as negative do not have the disease?". Those responses are provided by the positive and negative predictive values of the test.

The positive predictive value (PPV) of a diagnostic test is the proportion of those testing positive who truly have the disease. The higher the positive predictive value, the higher the likelihood that a person tested positive truly has the disease. The PPV is high when the specificity is high. A high prevalence of the disease or condition tested for in the population increases the PPV.

The negative predictive value (NPV) of a diagnostic test is the proportion of those testing negative who are truly disease free. The more sensitive a test, the less likely it is that a negative result will be a true positive - and hence the higher the negative predictive value. The higher the negative predictive value, the higher the likelihood that a person tested negative truly is disease free. A high prevalence of the disease or condition tested for in the population decreases the NPV.

Examples

The examples below show how to calculate sensitivity, specificity, positive predictive value and the negative predictive value. The examples also show that when the same test performs differently depending on the prevalence of the disease or condition tested for. If sensitivity and specificity are kept constant, the positive predictive value increases and the negative predictive value decrease with increasing prevalence. If the prevalence is low, a test with a good Se and Sp will have a low PPV. Even if only a small proportion of non diseased persons will have a positive test, those false positives will represent the majority of the positive tests. On the other hand the NPV will be high because false negatives will only represent a very small proportion of all negative results.

 Example 1: Prostate cancer test - medium prevalence (250/1000)

 

Cancer test 1

 

YES

NO

Test result

 Positive

200

25

225

 Negative

50

725

775

 

250

750

1000

 

 Sensitivity

 = 200 /  250

 = 0.80 or 80%

 Specificity

 = 725 / 750

 = 0.97 or 97%

 Positive predictive value

 = 200 / 225

 = 0.89 or 89%

 Negative predictive value

 = 725 / 775

 = 0.94 or 94%

 

Example 2: Prostate cancer low prevalence (125/1000)

 

 Cancer test 1

 

YES

NO

Test result

 Positive

100

26

126

 Negative

25

849

874

 

125

875

1000

 

 Sensitivity

 = 100 / 125

 = 0.80 or 80%

 Specificity

 = 849 / 875

 = 0.97 or 97%

 Positive predictive value

 = 100 / 126

 = 0.79 or 79%

 Negative predictive value

 = 849 / 874

 = 0.97 or 97%

 

Example 3: Prostate cancer - high prevalence (500/1000)

 

Cancer test 1

 

YES

NO

 Test result

 Positive

400

15

415

 Negative

100

485

585

 

500

500

1000

 

 Sensitivity

 = 400 / 500

 = 0.80 or 80%

 Specificity

 = 485 / 500

 = 0.97 or 97%

 Positive predictive value

 = 400 / 415

 = 0.96 or 96%

 Negative predictive value

 = 485 / 585

 = 0.83 or 83%

 

Back to overview

 

 1. Sheringham J, Kalim K, Crayford T. Mastering Public Health: A guide to examinations and revalidation. ISBN-13 978-1-85315-781-3