Imagine a study evaluating a new test that screens people for a disease. Each person taking the test either has or does not have the disease. The test outcome can be positive (predicting that the person has the disease) or negative (predicting that the person does not have the disease). The test results for each subject may or may not match the subject's actual status. In that setting:

True positive: Sick people correctly diagnosed as sick
False positive: Healthy people incorrectly identified as sick
True negative: Healthy people correctly identified as healthy
False negative: Sick people incorrectly identified as healthy

In general, Positive = identified and negative = rejected. Therefore:

True positive = correctly identified
False positive = incorrectly identified
True negative = correctly rejected
False negative = incorrectly rejected

In statistics and diagnostic testing, the positive predictive value, or precision rate is the proportion of positive test results that are true positives (such as correct diagnoses). It is a critical measure of the performance 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 outcome of interest, which may be unknown for a particular target population. The PPV can be derived using Bayes' theorem.

	Erstellt von Michael Riben vor etwa 11 Jahre

informaticsBoards NotForReview Notiz am HOMI15-MethodsforDecisionSupport, erstellt von Michael Riben am 18/08/2013.

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