Users of reviews are liable to be influenced by the choice of statistical presentations of the evidence. Hoffrage et al. suggest that physicians’ inferences about statistical outcomes are more appropriate when they deal with ‘natural frequencies’ – whole numbers of people, both treated and untreated – (e.g. treatment results in a drop from 20 out of 1000 to 10 out of 1000 women having breast cancer), than when effects are presented as percentages (e.g. 1% absolute reduction in breast cancer risk) (Hoffrage 2000). Probabilities may be more difficult to understand than frequencies, particularly when events are rare. While standardization may be important in improving the presentation of research evidence (and participation in healthcare decisions), current evidence suggests that the presentation of natural frequencies for expressing differences in absolute risk is best understood by consumers of healthcare information. This evidence provides the rationale for presenting absolute risks in ‘Summary of findings’ tables as numbers of people with events per 1000 people receiving the intervention.
Risk ratios and relative risk reductions remain crucial because relative effect tends to be substantially more stable across risk groups than does absolute benefit. Review authors can use their own data to study this consistency (Cates 1999, Smeeth 1999). Risk differences are least likely to be consistent across baseline event rates; thus, they are rarely appropriate for computing numbers needed to treat in systematic reviews. If a relative effect measure (OR or RR) is chosen for meta-analysis, then a control group risk needs to be specified as part of the calculation of an ARR or NNT. It is crucial to express absolute benefit for each clinically identifiable risk group, clarifying the time period to which this applies. Studies in patients with differing severity of disease or studies with different lengths of follow-up will almost certainly have different control group risks. In these cases, different control group risks lead to different ARRs and NNTs (except when the intervention has no effect). A recommended approach is to re-express an odds ratio or a risk ratio as a variety of NNTs across a range of assumed control risks (ACRs) (McQuay 1997, Smeeth 1999, Sackett 2000). Review authors should bear these considerations in mind not only when constructing their ‘Summary of findings’ table, but also in the text of their review.
For example a review of oral anticoagulants to prevent stroke presented information to users by describing absolute benefits for various baseline risks (Aguilar 2005). They presented their principal findings as “The inherent risk of stroke should be considered in the decision to use oral anticoagulants in atrial fibrillation patients, selecting those who stand to benefit most for this therapy" (Aguilar 2005). Among high-risk atrial fibrillation patients with prior stroke or transient ischaemic attack who have stroke rates of about 12% (120 per 1000) per year, warfarin prevents about 70 strokes yearly per 1000 patients, whereas for low-risk atrial fibrillation patients (with a stroke rate of about 2% per year or 20 per 1000), warfarin prevents only 12 strokes. This presentation helps users to understand the important impact that typical baseline risks have on the absolute benefit that they can expect.