Researchers develop a new test to detect the presence of a recently identified biomarker for hepatocellular carcinoma (HCC). The initial evaluation of the test shows the following: Which of the following is the likelihood that a patient with a negative test does not have HCC?

Question

Researchers develop a new test to detect the presence of a recently identified biomarker for hepatocellular carcinoma (HCC).  The initial evaluation of the test shows the following:  Which of the following is the likelihood that a patient with a negative test does not have HCC?

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11ec160b_3725_fb46_9249_95fbe7f42168_MD0001_00 The negative predictive value (NPV) of a diagnostic test is the probability (ie, likelihood) that an individual truly does not have the disease given a negative test. It is equal to the number of individuals who do not have the disease and who have a negative test result (ie, true negatives [TN]) divided by the total number of individuals with a negative test result (TN + false negatives [FN]). Therefore, NPV is calculated as: NPV = TN / (TN + FN) In this example, 120 individuals are TN and 5 individuals are FN; therefore, the total number of individuals with a negative test result (TN + FN) is 125 (ie, 120 + 5), and the test's NPV is calculated as follows: NPV = TN / (TN + FN) = 120 / (120 + 5) = 120 / 125 = 0.96 Positive and negative predictive values depend on the prevalence of the disease in the study population. For example, it is more likely that individuals who test positive truly have the disease and less likely that individuals who test negative do not have the disease in a high-prevalence population compared to a low-prevalence population. (Choice A) 0.10 is the false negative rate (FNR). FNR is equal to FN / (FN + TP) and describes the proportion of the individuals who really have the disease for which the test result is negative. It is also known as the miss rate. (Choice B) 0.20 is the false positive rate (FPR). FPR is equal to FP / (FP + TN) and describes the proportion of the individuals who really do not have the disease for which the test result is positive. It is also known as the fall-out rate. (Choice C) 0.60 is the overall proportion of TN in the entire study: TN / (TP + TN + FP + FN). (Choice D) 0.80 is the test's specificity. Specificity is equal to TN / (TN + FP) and describes the proportion of individuals who do not have the disease for which the test result is negative. It is an intrinsic measure of the test's ability to correctly identify individuals without the disease, but by itself, it does not provide enough information to interpret a negative test result in a particular individual. Educational objective: Negative predictive value (NPV) is the probability that an individual does not have a disease given a negative test result. It is equal to the number of individuals who do not have the disease and who have a negative test result (ie, true negatives [TN]) divided by the total number of individuals with a negative test result (TN + false negatives [FN]): NPV = TN / (TN + FN).

Researchers Develop a New Test to Detect the Presence of a Recently