Which bio-statistical term is positioned between positive predictive value and negative predictive value?

The term that is positioned between positive predictive value and negative predictive value is accuracy. In biostatistics, these values help us understand the effectiveness of a diagnostic test.

Accuracy combines both the true positive and true negative rates, reflecting the overall correctness of a test in discerning between those with and without a condition. It is calculated as the sum of true positives and true negatives divided by the total number of tests conducted. This metric provides insights into how well a test can identify both positive and negative cases, making it a comprehensive indicator of a test's performance.

In contrast, positive predictive value measures the proportion of true positive results in all positive results while negative predictive value refers to the proportion of true negative results in all negative results. Specificity refers to the ability of a test to correctly identify those without the disease (true negatives), and sensitivity indicates how well a test can identify those with the disease (true positives). While each of these terms contributes to evaluating a diagnostic test, accuracy serves as an aggregate measure situated between positive and negative predictive values.