Positive predictive value

Positive predictive value

The positive predictive value, or precision rate, or post-test probability of disease, is the proportion of patients with positive test results who are correctly diagnosed.It is the most important measure of a diagnostic method as it reflects the probability that a positive test reflects the underlying condition being tested for. Its value does however depend on the prevalence of the disease, which may vary.

Worked example

Definition

The Positive Predictive Value can be defined as

PPV = frac{ m number of True Positives} m number of True Positives}+{ m number of False Positives

or, alternatively,

PPV = frac{({ m sensitivity}) ({ m prevalence})}{({ m sensitivity}) ({ m prevalence}) + (1 - { m specificity}) (1-{ m prevalence})}

Problems with positive predictive value

Note that the PPV is not intrinsic to the test--it depends also on the prevalence. PPV is directly proportional to the prevalence of the disease /condition. In the above example, if the group of people tested had included a higher proportion of people with bowel cancer, then the PPV would probably come out higher and the NPV lower. If "everybody" in the group had bowel cancer, the PPV would be 100% and the NPV 0%.

Predictive values are often used in medical research to evaluate the usefulness of a diagnostic test. Hence the PPV is used to indicate the probability that in case of a positive test, that the patient really has the specified disease. However there may be more than one cause for a disease and any single potential cause may not always result in the overt disease seen in a patient.

An example is the microbiological throat swab used in patients with a sore throat. Usually publications stating PPV of a throat swab are reporting on the probability that this bacteria is present in the throat, rather than that the patient is ill from the bacteria found. If presence of this bacteria always resulted in a sore throat, then the PPV would be very useful. However the bacteria may colonise individuals in a harmless way and never result in infection or disease. Sore throats occurring in these individuals is caused by other agents such as a virus. In this situation the gold standard used in the evaluation study represents only the presence of bacteria (that might be harmless) but not a causal bacterial sore throat illness. It can be proven that this problem will affect positive predictive value far more than negative predictive value. To evaluate diagnostic tests where the gold standard looks only at potential causes of disease, one may use an extension of the predictive value termed the [http://www.infovoice.se/fou/epv Etiologic Predictive Value] . [cite journal |author=Gunnarsson RK, Lanke J |title=The predictive value of microbiologic diagnostic tests if asymptomatic carriers are present |journal=Statistics in medicine |volume=21 |issue=12 |pages=1773–85 |year=2002 |pmid=12111911 |doi=10.1002/sim.1119]

ee also

* Sensitivity and specificity
* Negative predictive value
* Relevance (information retrieval)
* Receiver-operator characteristic

References and notes

*


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