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Randomised trials are widely considered the ‘gold standard’ for causal inference, because on average randomisation balances covariates between treatment groups, even if those covariates are unobserved. However, trials are not immune to random confounding, as well as selection bias and measurement bias. Therefore, special care is needed in the design and analysis stages of randomised trials. Here, we review some important methodological aspects of randomised controlled trials in the context of a recently published paper, which assessed the effect of McKenzie method of mechanical diagnosis and therapy on pain and disability in patients with chronic, non-specific, low back pain using a randomised placebo-controlled trial.1
Method of randomisation
Garcia et al state that they randomly assigned 148 participants to two groups of similar sizes, that is, 74 patients per group using simple randomisation. However, simple (unrestricted) randomisation, equivalent to repeated fair coin tossing, can lead to treatment groups of markedly different sizes in small trials and thus imprecise effect estimates. In fact, the authors were very fortunate, as the probability of complete balance in their study is just 6.5%, and the probability of imbalances equal or greater than 10 (ie, 79 vs 69) is non-negligible (46.0%). Balanced block randomisation with, say, 37 blocks of size 4, would have insured balance in the number of patients being allocated to intervention or placebo in this study, although its sequence is more predictable than simple randomisation (to fix the latter problem, one can use larger block sizes, and randomly varying the block size). More importantly, balanced blocking prevents from substantial periodic imbalance and thus is often recommended for assignment in randomised trials.2 Successful randomisation also depends on allocation concealment, which authors achieved by use of sealed, opaque, numbered envelopes. Failure to conceal the …
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