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Board Review

Board Review. Medical Statistics November 2009.

lillian-kerr
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Board Review

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  1. Board Review Medical Statistics November 2009

  2. 44. In a study to evaluate a test as a screen for the presence of a disease, 235 of the 250 people with the disease had a positive test and 600 of the 680 people without the disease had a negative test. Based on this data, the specificity of the test for the disease is A) 235/250 = 94% B) 15/250 = 6% C) 600/680 = 88% D) 80/680 = 12% E) 15/80 = 19%

  3. ANSWER: C The specificity of a test for a disease is the proportion or percentage of those without the disease who have a negative test. In this case, option A is the sensitivity, i.e., the proportion of those with the disease who have a positive test. Option B is the false-negative rate and option D is the false-positive rate. Option E is the ratio of false-negative tests to false-positive tests, a meaningless ratio. The predictive values of positive and negative tests are extremely important characteristics of a screening test. Determination of these values requires knowledge of the prevalence of the disease in the population screened, as well as the sensitivity, specificity, and false-positive and false-negative rates. Since the prevalence of most diseases is low, the percentage of those with a positive test (the predictive value of a positive test) is relatively low, even when sensitivity and specificity are high. When prevalence is low, however, the predictive value of a negative test is very high and may approach 100%.

  4. 111. Which one of the following best defines the sensitivity of a diagnostic test for a particular disease? A) The test’s accuracy in correctly identifying patients without the disease B) The test’s accuracy in correctly identifying patients with the disease C) The difference between the false-positive and false-negative rates D) A value calculated from the test’s specificity

  5. ANSWER: B Sensitivity is the ability of a test to identify patients who actually have the disease, or the true-positive rate. Independent of the sensitivity is the test’s specificity, which is the ability to correctly identify patients who do not have the disease, or the true-negative rate. The greater the test’s specificity, the lower the false-positive rate; the greater the test’s sensitivity, the lower the false-negative rate.

  6. 176. Information derived from which one of the following provides the best evidence when selecting a specific treatment plan for a patient? A) Meta-analysis B) Prospective cohort studies C) Expert opinion D) Consensus guidelines

  7. ANSWER: A In general, the strongest evidence for treatment, screening, or prevention strategies is found in systematic reviews, meta-analyses, randomized controlled trials (RCTs) with consistent findings, or a single high-quality RCT. Second-tier levels of evidence would be poorer quality RCTs with inconsistent findings, cohort studies, or case-control studies. The lowest quality of evidence would come from such sources as expert opinion, consensus guidelines, or usual practice recommendations.

  8. 3. The specificity of a screening test is best described as the proportion of persons A) with the condition who test positive B) with the condition who test negative C) with the condition who test positive, compared to the total number screened D) without the condition who test positive E) without the condition who test negative

  9. ANSWER: E A screening test’s specificity is the proportion of persons without the condition who test negative for that condition. In other words, it is a measure of the test’s ability to properly identify those who do not have the disease. Conversely, the sensitivity of a screening test is the proportion of those with the condition who test positive. The other options listed describe false-negatives, false-positives, and prevalence.

  10. 141. Results of a clinical study show a relative risk reduction (RRR) of 33% and an absolute risk reduction (ARR) of 20%. There are 1000 patients each in the treatment and control groups. To help determine the potential benefit of the treatment it is necessary to identify the number needed to treat (NNT). Which one of the following is the NNT for this clinical study? A) 3 B) 5 C) 13 D) 130 E) The number cannot be determined from the information provided

  11. ANSWER: B To a practicing physician, the number needed to treat (NNT) is one of the most useful calculations for assessing the benefit of a treatment. Simplified, the NNT is the number of patients necessary to treat in order for one patient to benefit. The relative risk reduction (RRR), often quoted in the press or by those promoting a treatment, can be misleading to both the general public and to physicians. Much more useful is the absolute risk reduction (ARR); NNT is the mathematical reciprocal of ARR (i.e., 1/ARR).

  12. 50. Statistics used for evaluating a study include relative risk reduction (RRR), absolute risk reduction (ARR), and number needed to treat (NNT). Which one of the following is the correct formula for calculating NNT? A) 1/(RRR + ARR) B) 1/RRR C) 1/ARR D) 100 × ARR E) 100 × RRR

  13. ANSWER: C The absolute risk reduction (ARR) is the difference in the outcome event rate between the control group and the experimental group in a given study. The relative risk reduction (RRR) is the percent reduction in the measured outcome between the experimental and control groups. In evaluating a clinical study, the single most clinically useful statistic may be the number needed to treat (NNT). The NNT is the number of patients who must be treated to prevent one adverse outcome, or the number of patients who must be treated for one patient to benefit. The NNT is simply the inverse of the ARR. Thus, 1/ARR is the correct formula for calculating NNT.

  14. 17.The positive likelihood ratio of a test is defined as the ratio of A/B, where A is the proportion of patients who truly have the disease and a positive test result, and B is the proportion of patients without the disease who still have a positive test. How is this expressed in standard statistical terminology? A. Sensitivity/specificity B. Positive predictive value/negative predictive value C. Specificity/positive predictive value D. Sensitivity/(1 minus specificity)

  15. 17-D. Using the definition outlined in the question, the correct formula for positive likelihood ratio can be determined from the standard 2-square x 2-square: The value of "A" can be equated to the sensitivity of the test (the proportion of patients with a positive test who truly have the disease), or a/(a + c); and "B" can be equated to 1 minus specificity (the proportion of patients without the disease who still have a positive test), or b/(b + d). The positive likelihood ratio indicates how much a positive test raises or lowers the pretest probability of the presence of disease. A test with a positive likelihood ratio of 1 does not change the probability of the presence of disease and is, therefore, an essentially useless test—you may as well flip a coin. The larger the positive likelihood ratio of a test, the more valuable it is in increasing the likelihood of the presence of disease; the closer the positive likelihood ratio is to zero, the more valuable it is in decreasing the likelihood of the presence of disease. Note that the positive likelihood ratio cannot be zero, unless the sensitivity of the test is also zero.

  16. EBM Statistics • SN - Percentage of patients with disease who have a positive test for the disease in question. A negative test with high sensitivity rules out the disease in question. (SNOUT) A test with high sensitivity has few false negatives. • SN = TP / (TP + FN) • SP - Percentage of patients without disease who have a negative test for the disease in question. A positive test that is highly specific rules in the disease. (SPIN). A test with high specificity has few false positives. • SP = TN / (TN + FP)

  17. 21. The number needed to treat (NNT) using a given therapeutic intervention can be calculated as: A. 1/Absolute risk reduction B. 1/Relative risk reduction C. Positive predictive value minus negative predictive value D. 1/(sensitivity minus specificity)

  18. 21-A. The NNT is the number of patients who must receive an intervention or therapy (usually for a specific period of time) to prevent 1 adverse outcome or produce 1 positive outcome. It is easily calculated as the inverse of the absolute risk reduction (expressed as a decimal number, not as a percentage). As an example, if the risk of death 1 year after a small myocardial infarction in a 40-year-old man who is treated with streptokinase (Streptase) is 2%, and his risk of death when treated with tissue plasminogen activator (tPA) is 1.76%, the NNT is 1/(0.02 -0.0176), or 1/0.0024, or about 417. The relative risk reduction is 12% in this case, a seemingly good reduction, but the NNT is quite large and would imply that the true benefit of tPA in this patient is perhaps not worth the added expense, and that streptokinase may be a better choice. NNT is the most easily understood statistic by both clinicians and patients for explaining the benefit of a specific therapy. The magnitude of NNT for some common medical interventions is also startlingly large.

  19. EBM Statistics • RRR - The percentage difference in risk or outcomes between treatment and control groups. Example: if mortality is 20 % in controls and 10 % with treatment • RRR is (20-10)/20 = 50 % • ARR - The arithmetic difference in risk or outcomes between treatment and control groups. Example: if mortality is 20 % in controls and 10 % with treatment • ARR is 20-10 = 10 % • NNT - The number of patients who need to receive an intervention instead of the alternative in order for one additional patient to benefit. • NNT = 1/ARR (%) = 1/0.10 or 100/10 = 10

  20. EBM Statistics • PPV – Percent of patients with positive test having disease. Puts Test Specificity in context of disease Prevalence. • PPV = TP / (TP + FP) • NPV – Percent of patients with negative test who do not have disease. Puts Test Sensitivity in context of disease Prevalence. • NPV = TN / (TN + FN)

  21. Disease Yes No + PPV TP/ (TP+ FP) a/a+b Test - NPV TN/ (TN+ FN) d/c+d Sensitivity Specificity TP/ (TP+ FN) a/a+c TN/ (TN+ FP) d/b+d

  22. A p-value of < 0.05 means: There is less than a 5 % probability that the study results occurred by chance alone • A 95% confidence interval indicates that the true population mean is expected to be within this range in 95 of 100 similar samples

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