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Clinical trials – reading between the lines.

11 Oct, 13 | by Bob Phillips

Another new series of blogs here in the ADC website, from Ian Sinha of the Respiratory Unit, Alder Hey Children’s Hospital, Liverpool, UK, takes a look at explaining the deeper depths of critical appraisal of randomised controlled trials from the perspective of the Cochrane collaboration’s approach to this issue.

– Archi


Clinical trials – reading between the lines

Just because a research study is called a “double-blind randomized controlled trial” (RCT), this may not be an accurate description. Even if it is a double-blind RCT, this does not mean it is scientifically robust.


Reading between the lines part 2: Some ‘equal groups’ are more equal than others

18 Oct, 13 | by Bob Phillips

Selection bias – some ‘equal groups’ are more equal than others

The groups of participants receiving interventions should be equal, otherwise confounding variables might give one treatment an advantage over another. If there is a systematic reason for this, the study is at risk of selection bias.


Randomization (sequence generation)

The first consideration is whether the treatments were allocated randomly. The best method is to predefine a randomization schedule, in which treatments are allocated by chance alone. Other methods could introduce differences between groups. An example with obvious implications (to highlight the point) would be to randomize preterm boys to one group, and girls to another. It might seem reasonable to allocate treatments, after participants enrol, by flipping coins or rolling dice, but this might make group sizes unequal. ‘Quasi-randomisation’ usually implies that patients receive treatments based on pre-randomisation assessment, which by definition introduces differences between groups, leading to particularly high risk of bias.


Reading between the lines part 3: Hiding who got what

25 Oct, 13 | by Bob Phillips

Performance and detection bias – hiding who got what

Bias can occur if the treatment arm to which a given participant is randomized is known. When reading an RCT report, the term “double-blind” is often not sufficient to help appraise this. We need to know from whom treatment identity was masked, and how.


Reading between the lines part 4: Cherry-picking the best results

2 Nov, 13 | by Bob Phillips

Outcome reporting bias: cherry-picking the best results

When planning an RCT, the choice of primary outcome is crucial. This is an integral part of the research question, and forms the basis of the sample size calculation. Secondary outcomes are also chosen, to give a wider indication of the effects of interventions, generate new hypotheses, and contribute to meta-analyses.

In many trials, outcomes are selectively reported, on the basis of the results, or the reported primary outcome differs from the one specified at the trial outset (again, often changed on the basis of the results). These practices lead to outcome reporting bias.

Selective outcome reporting renders the study report a biased reflection of the overall trial findings, which is misleading for the reader, and has substantial impact on the conclusions of Cochrane reviews (whose meta-analysis comprises positive results, but not all the negative results).


It can be difficult to evaluate for outcome reporting bias in an individual trial. It is important to check that all outcomes described in the methods are subsequently accounted for in the results. Consider whether other outcomes you might have expected to see are missing. It is also useful to check the trial protocol (if available) to check for outcomes that were measured, and not reported. In some conditions, core outcome sets have been agreed (which should be measured and reported in all trials in that condition), and it may be useful to compare these with what was reported in the trial.



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