In the same study as described in question 5, what are the odds of developing AUR in the placebo arm?
Answer D
It is important to be able to distinguish between risk (probability) and odds as they are both often used in published studies, but are not interchangeable. The risk, or probability, is defined as the number of subjects experiencing the outcome of interest divided by the total population at risk/being studied. It can either be expressed as a value between 0 (never occurred) to 1 (always occurred) or as a percentage. This contrasts to odds which equals the probability of an event occurring divided by the probability that it does not. Odds and odds ratio (odds in ‘exposed’ group divided by odds in ‘unexposed’ group) are often used in studies reporting on binary outcome variables (e.g., mortality).
Publication bias is best detected in a meta-analysis using which of the following graphical forms?
Answer A
Funnel plots graphically display a measure of study precision (vertical axis) against treatment effects for each of the studies included in the meta-analysis. The resultant symmetry or indeed asymmetry can give an indication of the presence of publication bias. Publication bias describes the phenomenon whereby systematic reviews and meta-analysis do not necessarily include all available studies of interest; smaller studies without statistically significant results are less likely to be published. In the absence of publication bias, a symmetrical inverted funnel (hence the name funnel plot) is generated because the treatment effect estimates from smaller studies scatter more widely at the base of the graph with those of larger studies narrowing towards the apex. If a meta-analysis is over-represented by larger studies with more significant results, and therefore, smaller studies are not included, asymmetry of the ‘funnel’ occurs. Funnel plots are commonly used by the Cochrane collaboration reviews. Receiver operator characteristic (ROC) curve is a plot of a test’s (with a binary outcome) sensitivity (y-axis) against 1 – specificity (x-axis) using different cut-off values. By calculating the area under the curve, the accuracy (discriminatory ability) of the test can be determined. Forest plots are commonly used in a meta-analysis of randomised controlled trials and is a way of presenting the outcome data for each RCT included in the meta-analysis (including the confidence intervals and weight of each RCT in the meta-analysis) and provides a summary estimate. Histograms are used to illustrate frequency distributions of data.
Which of the following statements is INCORRECT when referring to a p-value?
Answer B
A p-value is generated by performing a hypothesis (significance) test and provides a measure of how confident one can be in rejecting the null hypothesis. A commonly used cut-off for a statistically significant result is <0.05 and with a p-value below this, the null value will not be within the 95% confidence limits. At this level, there is less than a 5% probability that the observed effect could have resulted by chance if the null hypothesis were true. The confidence interval should always be interpreted alongside the p-value, as even with a very significant result (very low p-value), the confidence interval may indicate no important difference between groups.
A Serious Incident does NOT need to be declared for investigation in which of the following instances?
Reporting of serious incidents in the NHS should be aligned to the national framework produced by the National Patient Safety Agency ‘Serious Incident Reporting and Learning Framework (SIRL)’. This framework was introduced to ensure consistency in definitions of serious incidents, roles and responsibilities and clarify legal and regulatory requirements. A full list of serious incidents requiring investigation are available in the document ‘National Framework for Reporting and Learning from Serious Incidents Requiring Investigation’.
This test is used to compare the medians of two groups of independent non-parametric numerical data
Spearman correlation is used to establish an association between non-normally distributed numerical variables. Chi-square is the test that compares the proportion of people with a particular attribute in two or more independent groups of categorical data. T-test is used to compare the means of two groups of parametric numerical data. Pearson correlation is used to determine the strength of a relationship of a continuous normally distributed variable amongst two groups.