Predictive Values

Predictive values in diagnostic testing, are the proportion of true positives and true negatives. The positive predictive value (PPV) and negative predictive value (NPV) describe the accuracy of a diagnostic test; however, unlike sensitivity and specificity, predictive values are largely dependent on the dysfunctions prevalence in the examined population (1). For example, imagine a patient who just tested positive on a special test. If the test was positive, what is the probability that they really have that dysfunction; how worried should they be? Now, imagine the same test performed on a very different individual in a different setting with very different subjective indicators. How do you think that would effect predictive value?
  • Positive predictive value (PPV) - describes the probability of having the dysfunction of interest in a subject with a positive result. Therefore PPV represents the proportion of patients with positive test result that are positive in a given population.
  • Negative predictive value (PPV) - describes the probability of not having the dysfunction of interest in a subject with a negative test result. Therefore NPV represents the proportion of patients without the disease that are negative in a given population.
  1. Šimundić, A. M. (2009). Measures of diagnostic accuracy: basic definitions. Ejifcc19(4), 203

Related Terms

  • Negative predictive value (NPV)
  • Sensitivity
  • Specificity
  • Prevalence
  • False positive
  • False negative


  • positive predictive value
  • negative predictive value
  • PPV
  • NPV
  • predictive value