5.5.1.1. Categorical outcomes

As we explained before, it is good to discuss the clinical decision threshold and try to determine them on beforehand. However, one must be careful not to apply this in a too mechanical way, as you need also to take into account the downsides of the intervention, such as side effect or morbidity induced by the intervention, on which in a lot of cases you have only information after collecting the evidence. If it is not possible to determine a threshold, then GRADE suggests to use a RRR or RRI greater than 25%. This 25% threshold is a good and pragmatic starting point, but other factors should be taken into account as well, such as the absolute risk reduction.

5.5.1.2. Continuous outcomes

If the 95%CI upper or lower CI crosses the minimally important difference (MID), either for benefit of harm.

Note: if the MID is not known or the use of different outcome measures required calculation of an effect size, we suggest downgrading if the upper or lower CI crosses an effect size of 0.5 in either direction.

Effect size (ES):

A generic term for the estimate of effect of treatment for a study. Sometimes, the term is used to refer to the standardized mean difference, the difference between two estimated means divided by an estimate of the standard deviation.

To facilitate the understanding we suggest an interpretation of the effect size offered by Cohen, also known as ‘Cohen’s d’[1]. According to this interpretation, an effect size or SMD of around:

• 0.2 is considered a small effect;
• 0.5 is considered a moderate effect;
• 0.8 or higher is considered a large effect.

There are also other methods to standardise effect sizes, such as Glass ∆ or hedges d, depending on the choice of the estimator of the standard deviation.

[1] (Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed; 1988).