Читать книгу Evidence in Medicine - Iain K. Crombie - Страница 36

Small studies are often referred to as having low power. In medical research, statistical power refers to the chances (probability) that a study will detect a significant effect of treatment if one truly exists. A power of 80% is recommended, but few trials in medicine achieve this: in an overview of 136,000 trials only 9% of those published between 2010 and 2014 did so [86].

A consequence of low power is that spuriously significant results are more likely to occur [87]. Another problem is that, if there is a real benefit of treatment, small studies are unlikely to detect the benefit as being statistically significant. These apparently conflicting statements are true because chance is even‐handed; it will make some interventions appear to have a larger effect size than they do in reality, and will sometimes make the effect seem smaller than it really is [79]. Inflated effect sizes are more likely to be significant and reduced ones less so. The result is that small trials have much more heterogeneous effect sizes than large ones [88].

To ensure that the trial has adequate power, researchers should carry out a formal sample size calculation (specifying the likely size of the treatment effect as well as the required power and an estimate of variance). The frequency of reporting sample size calculations is often low. For example, only 41% of low back pain trials [89] and 35% neurosurgical trials [90] reported this calculation. Even when the sample size calculation is reported, researchers often overestimate the possible benefit of the treatment and end up with sample sizes that are too small to detect a clinically realistic effect [89, 91].