Given a test that's positive, the disease is present.

The probability that the disease is really present when the test is positive. (see Sensitivity, Specificity and Negative Predictive Value).

The probability that an individual with a positive test has, or will develop, a particular disease, or characteristic, that the test is designed to detect.

is the proportion of people with a positive test who have disease. Also called the post-test probability of disease after a positive test See also SpPins and SnNouts.

For a diagnostic procedure, the conditional probability of disease given a positive test result.

(synonym: PPV) The likelihood that an individual with a positive test result actually has the particular gene in question, is affected, or will develop the disease

The probability that a test-positive individual is truly infected.

The probability that the condition of interest is true if the result is positive – for example, the probability that the disease is present given a positive test result.

The probability that a person with a positive test result has, or will get, the disease for which the analyte is used as a predictor.

is the proportion of people with a positive test who have disease. See also Biostatistics and Prevention.

The probability that an individual with a positive test for a disease actually has it. A high positive predictive value indicates that the patient who has a positive test result probably has the disease.

(For screening tests), the proportion of individuals screened positive by the test who actually have the disease. ( 4-18)

Proportion of people with a positive test who have the target disorder. See also likelihood ratio.

is the proportion of people with a positive test who have disease. See also Calculating Sensitivity and Specificity.

The probability that a subject has the disease when the test result is positive. Synonyms include predictive value positive. Positive predictive value = a/m1 = TP/(TP+FP). By application of Bayes' Rule, the positive predictive value also can be defined as a function of pretest probability of disease (p), sensitivity, and specificity: positive predictive value = [(p . sensitivity)/[p . sensitivity + (1-p) . (1- specificity)].

Proportion of true positive tests when a modality gives a positive test result.

The positive predictive value is the proportion of patients with positive test results who are correctly diagnosed. It is considered the physician's gold standard, as it reflects the probability that a positive test reflects the underlying condition being tested for.