What is the name for the proportion of positive test results that accurately identify true positives?

The proportion of positive test results that accurately identify true positives is known as positive predictive value (PPV). This statistic reflects the probability that individuals who receive a positive test result actually have the condition being tested for. In clinical practice, a high positive predictive value indicates that the test is effective at correctly identifying those with the disease, which is crucial for making informed treatment decisions and minimizing false-positive results.

Positive predictive value is influenced by the specificity of the test along with the prevalence of the disease in the population being tested. When the disease is more prevalent, the positive predictive value increases, as more of the positive results are likely to be true positives. Thus, understanding PPV is vital for assessing test accuracy and ultimately improving patient care and outcomes.

The other concepts such as negative predictive value, incidence, and odds ratio pertain to different aspects of epidemiology and diagnostic testing. Negative predictive value pertains to the likelihood that individuals with a negative result truly do not have the disease. Incidence refers to the number of new cases of a disease in a given population over a specified period. The odds ratio is a measure of association used in case-control studies to compare the odds of an outcome occurring in an exposed group versus a non-exposed group. Each of these