If the area under the curve (AUC) for the new test was found to be 0.5, what does it mean?
C. The primary statistical measure obtained from the ROC is the AUC. The AUC value can be used to compare with the AUC value of a curve corresponding to the null hypothesis. The null hypothesis is represented by a curve that could be obtained if the test has no usefulness in discriminating those with the diagnosis and those without. This hypothetical curve will then have an AUC of 0.50, which corresponds to the area in the graph that falls below the dotted line. The difference in the two AUC consists of the area of the graph between the dotted line and the curve. The AUC can be interpreted in another very useful way. AUC is the probability that the test will show a higher value for a randomly chosen individual with depression than for a randomly chosen individual without depression. That means, if we fi nd the AUC for this particular test was 0.9 and take two individuals at random, one with and one without depression, the probability that the first individual will have a higher score than the second is nearly 90%. Fortunately, the AUC, the sensitivities and specificities, and the whole ROC are calculated by statistical software, sparing us of the burden.
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A meta-analysis of seven RCTs that compared a new antidepressant X with placebo was conducted. Effect size analysis for the change in HAMD scores are shown in the graph above. With respect to the graph below, answer Questions 97–99.
What is the name of the graph shown above?
E. Meta-analysis are usually displayed in graphical form using Forest plots, which present the findings for all studies plus (usually) the combined results. This allows the reader to visualize how much uncertainty there is around the results. The graph in question, modified below, presents a Forest plot, sometimes called a ‘blobbogram’ identifying its basic components.
How many studies in the meta-analysis show statistically significant advantage for the new antidepressant?
C. As shown in the diagram above, the horizontal lines along with the ‘blobs’ show the 95% confidence intervals of the effect size or each study. If the confidence intervals cross the line of no effect (at 0 in this case), it suggests that the effect is not statistically significant. Out of the seven studies, the confidence intervals of three of the effect sizes of three of the trials (1, 2 and 5) cross the line of no effect, and four (trials 3, 4, 6 and 7) do not cross the line. The summary measures in cases of dichotomous variables are usually odds ratios, and the line of no effect in that case will correspond to 1.
Which of the trials has the greatest weight on the overall analysis?
D. The size of the blobs (lozenges) in the blobbogram usually represents the size of the study, or more exactly the proportion of the weight that the study contributes to the combined effect. In this case, the largest blob is that of trial 6.
In which of the following situations is sensitivity analysis especially recommended while conducting a meta-analysis?
D. A systematic exploration of the uncertainty in the data is known as sensitivity analysis. It is carried out to measure the effects of varying study variables such as individual sample size, number of positive trials, number of negative trials, etc., on expected summary outcome measure of a study (often a meta-analysis or economic study). Sensitivity analysis can be undertaken to answer the question, ‘Is the conclusion generated by a meta-analysis affected by the uncertainties in the methods used?’ One such uncertainty is publication bias. So, we can use sensitivity analysis to fi nd out the impact of having missed unpublished studies.
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