A research laboratory is evaluating a new dipstick test for diagnosing urinary tract infections (UTIs). The new test is compared to urinalysis of a midstream urine specimen (considered the diagnostic gold standard) to establish diagnostic test parameters. The study enrolls 300 patients, of which 100 have a UTI as determined by urinalysis. The new dipstick test is determined to be 70% sensitive and 90% specific for the diagnosis of UTIs. How many false positives are present in the study?

Question

A research laboratory is evaluating a new dipstick test for diagnosing urinary tract infections (UTIs).  The new test is compared to urinalysis of a midstream urine specimen (considered the diagnostic gold standard) to establish diagnostic test parameters.  The study enrolls 300 patients, of which 100 have a UTI as determined by urinalysis.  The new dipstick test is determined to be 70% sensitive and 90% specific for the diagnosis of UTIs.  How many false positives are present in the study?

Quizplus · Accepted Answer

11ec160b_3721_8e64_9249_d7353714f51b_MD0001_00 Sensitivity = a/(a + c); Specificity = d/(b + d) False positives describe people who test positive for a disease but do not actually have it. The number of false positives can be calculated using the test's specificity and total number of patients without urinary tract infections (UTIs). The specificity of a test refers to the number of true negatives (d) divided by the total number of patients without the disease (b + d). In this case, the specificity is 90% (ie, 0.9) and the total number of patients without UTIs (b + d) equals 300 - 100 = 200. Using this information, the number of true negatives (d) can be calculated: The total number of patients without the disease (b + d) equals 200, and the number of true negatives (d) equals 180. Using this information, the number of false positives (b) can be calculated: For the sake of thoroughness, the 2 × 2 table can be completed as follows. Sensitivity refers to the number of true positives (a) divided by all people with the disease (a + c). Repeating the same exercise as above gives the completed table: The number of true negatives and false positives can be more quickly calculated using the following equations: Educational objective: Specificity is the number of true negatives divided by the total number of subjects confirmed as not having the disease.

A Research Laboratory Is Evaluating a New Dipstick Test for Diagnosing