A patient with symptoms suggestive of a rare infectious disease undergoes a diagnostic test. The test correctly identifies 95% of individuals with the disease. Which of the following terms best describes the probability that a positive test result truly indicates the presence of the disease?
C) Positive predictive value
Positive predictive value is the probability that a positive test result truly indicates the presence of the disease. It is calculated as true positives/(true positives + false positives). In this case, it represents the likelihood that a positive test accurately indicates the presence of the rare infectious disease in the patient with symptoms.
Answer choice A: False positive rate, is incorrect. The false positive rate is the proportion of individuals without the disease who receive a positive test result. It is calculated as false positives/(false positives + true negatives).
Answer choice B: Negative predictive value, is incorrect. Negative predictive value is the probability that a negative test result truly indicates the absence of the disease. It is calculated as true negatives/(true negatives + false negatives).
Answer choice D: Sensitivity, is incorrect. Sensitivity is the probability of a positive test result when the disease is present. It is calculated as true positives/(true positives + false negatives).
Answer choice E: Specificity, is incorrect. Specificity is the probability of a negative test result when the disease is absent. It is calculated as true negatives/(true negatives + false positives).
Key Learning Point
Positive predictive value is the probability that a positive test result truly indicates the presence of the disease. It is calculated as true positives/(true positives + false positives). Positive predictive value is crucial for understanding the likelihood that a positive test accurately reflects the presence of a particular condition in a given patient.
