Basic statistical concepts
Statistical principles
In his autobiography, Mark Twain identified three types of lies: “lies, damned lies, and statistics.” (Twain, 2012) The role of statistical misrepresentation has continued into modern medicine, as well. In an interview with the Atlantic, Dr John Ioannidis had this to say about current scientific rigor and bias, “At every step in the process, there is room to distort results, a way to make a stronger claim or to select what is going to be concluded.” (Freedman, 2010) In September 2014, JAMA published a review of the re-analysis of randomized clinical trial data; 35% of had changed conclusions based on independent review of the data. (Ebrahim et al., 2014) With medicine’s current focus on delivering improved quality care through the use of evidenced based medicine*, clinicians should have a basic understanding of key statistical concepts.
Sensitivity
At its most basic, sensitivity is a measurement of well one’s test does finding the people with the disease in the entire population. In a test with low sensitivity, there will be people in the screened population which have a negative test despite having the disease. In a highly sensitive test, everyone in the tested population that has the disease will have a positive test. Positive disease Negative disease Positive test True positive (A) False positive (B) Negative test False negative (C) True negative (D)
Looking at the results of figure one, you can calculate the sensitivity of a test by dividing the true positive results over the entire population with the disease. Done mathematically, the sensitivity is A/(A+C). Applying this to clinical research, one could look at the prostate screening antigen (PSA). PSA goes up in cases of prostate cancer, so it has been used as a screening test. Initially, a PSA level of 4 ng/ml or higher was considered a positive screen. Because there were cases of prostate cancer missed with this cutoff, there was a discussion of lowering the threshold to 2.5 ng/ml. This would decrease the number of missed patients with prostate cancer, thereby increasing the sensitivity of the test. (Welch, Schwartz, & Woloshin, 2005) Not only was the level not lowered to 2.5 ng/ml, the US Preventive Service Task Force recommended against screening for PSA at all. This has to do in part with the “specificity” of the PSA screen. (http://www.uspreventiveservicestaskforce.org/Page/Document/RecommendationStatementFinal/prostate-cancer-screening#citation16)
Specificity
Whereas sensitivity focused on not missing cases of the disease, specificity focuses on a positive test result meaning that the patient has it. A test with low specificity, such as PSA, has many positive test results where the individual does not have the disease. The newborn screen for phenylketonuria is over 99% specific. (Kwon & Farrell, 2000)
