Clinical Trials

Clinical Trials: A Practical Guide ■❚❙❘• excluded patient populations, eg, heart failure patients over 65 years oldor with renal disease• treatment strategies, eg, beta-blockers versus placebo or diuretics• primary outcomes, eg, mortality or hospital readmissionsIn general, meta-analyses favor randomized double-blind trials, as biases areminimized or distributed evenly by the process of random allocation. More liberalinclusion criteria can make the studies broader (allowing retrospective ornon-randomized studies) but the conclusions might be more subjective [5].Ideally, the inclusion criteria must aim to address the main research question [6].For example, a study looking at the treatment effect on quality of life shouldexclude trials that do not use quality of life as an outcome.The researcher should also aim to identify negative or indifferent studies thatmight not have been published, or studies awaiting publication. Research registerscan be consulted and well-known researchers can be contacted directly. Hospitalswith an interest in the condition under examination might be aware of ongoingtrials or unpublished data.Publication biasPublication bias is an immediate problem facing researchers conducting athorough meta-analysis, since journals prefer to publish trials with significantpositive findings rather than trials with negative or indifferent findings. Theexistence of publication bias can be inferred by constructing a funnel plot [7].A funnel plot is a simple scatter plot of the treatment effects (such as odds ratios[ORs]) estimated from individual studies on the x-axis, against a measure of theprecision of each study (such as sample size or standard error) on the y-axis.The name ‘funnel plot’ arises from the fact that estimation of the true treatmenteffect by each component study becomes more precise as the sample sizes of thecomponent studies increase. Therefore, small studies will produce estimates ofthe effects that will scatter more widely at the bottom of the graph, and willconverge for larger studies if these are well matched. In the absence of publicationbias, the plot should resemble a symmetrical inverted funnel. A funnel plot for thedata in Table 1 is given in Figure 1; it shows no evidence of asymmetry orpublication bias. For more about publication bias and funnel plots, please refer toreferences [4] and [7].Step 4: Data extraction and quality assessmentOnce the final set of studies is identified, the relevant information must beextracted from each study and an assessment of the quality of the available datamust be made. Information on study design, patient characteristics, treatments,443