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Positive and Negative predictive value for a test
Learning ObjectivesPositive and Negative predictive value for a test
Predictive values are important for a diagnostic test because they tell how truly a test result reflects the truth. Positive predictive value (PPV) and negative predictive value (NPV) are true positive and true negative results of a diagnostic test, respectively. In other words, if someone receives a certain diagnosis by a test, predictive values describe how likely it is for the diagnosis to be correct.
Positive Predictive Value
PPV is the percentage of patients with a positive test who actually have the disease.
It is the ratio of patients truly diagnosed as positive to all those who had positive test results (including healthy persons who were incorrectly diagnosed as patients). More the PPV of a diagnostic test, a positive test result is more likely that the person is truly a patient.
Negative Predictive Value
NPV is the percentage of patients with a negative test who do not have the disease.
It is the ratio of patients truly diagnosed as negative to all those who had negative test results (including patients who were incorrectly diagnosed as healthy). More the NPV of a diagnostic test, a negative test result is more likely that the person truly doesn't have the disease.
Calculation of NPV and PPV
| | Disease Present | Disease Absent | |
| Test Result Positive | True Positive (TP) | False Positive (FP) | Positive Predictive Value=TP/(TP+FP) |
|
Test Result Negative |
False Negative (FN) | True Negative (TN) | Negative Predictive Value=TN/(TN+FN) |
Example of PPV and NPV:
Imagine we have a sample population of 100 people, with test result of X diagnostic test distributed as shown in Table 2, the PPV and NPV of the test are as follows:
| | Disease Present | Disease Absent | PPV/NPV | Interpretation |
| Test Result Positive | True Positive (TP) =75 | False Positive (FP) =15 | Positive Predictive Value=75/(75+15) = 83.3% | PPV: This means that in this population, 83.3% of people whose test result is positive, have the disease. |
|
Test Result Negative |
False Negative (FN) = 25 | True Negative (TN) = 85 | Negative predictive value=85/(85+25) = 77.3% | NPV: This means that in this population, 77.3% of the people whose test result is negative, are healthy. |
In this case, the diagnostic test X has a PPV of 83.3% and an NPV of 77.3%. We can say that the test X can diagnose the disease more precisely than ruling it out. It is NOT necessary that if a test has higher PPV, it should also have higher NPV. If both the predictive values are close to 100, the diagnostic test is considered a gold standard.
Resources
- Understanding and using sensitivity, specificity and predictive values; Rajul Parikh et al., 2008
- Fletcher RH, Fletcher SW, Fletcher GS. Clinical epidemiology: the essentials. Lippincott Williams & Wilkins; 2012
- Diagnostic Tests; Predictive Values, Altman
Assessment
| Question | Answer 1 | Answer 2 | Answer 3 | Answer 4 | Correct answer | Correct explanation | Page id | Part of Pre-test | Part of Post-test |
| Which of the following is true about predictive values? | PPV is the percentage of patients with a negative test who do not have the disease. | PPV is the percentage of patients with a positive test who actually have the disease. | NPV and PPV are not useful characteristics of diagnostic tests. | None of the above |
2
|
PPV is the percentage of patients with a positive test who actually have the disease. | Yes |
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