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Since 1992, these seven criteria have been widely used to assess hypothesised subgroup effects,14 15 16 17 18 19 20 21 22 23 and have undergone only minimal cosmetic revisions.4 After years of use of the 1992 criteria, we had begun to perceive limitations.These limitations became vivid when deciding on the credibility of a subgroup hypothesis of a large multi-centre randomised trial.24 On the basis of this experience, a review of published methodological articles addressing subgroup analyses, and consultation with clinicians and epidemiologist colleagues, we identified four new criteria that could further aid differentiation between spurious and real subgroup effects.Compare this woman to a 65 year old smoking male with a family history of heart diseases and diabetes, presenting with a raised serum cholesterol ( 6.2 mmol/L), and blood pressure of 160/90 mm Hg. A meta-analysis showed that statin therapy could reduce the relative risk of major coronary events by 29.2%.25 This relative effect was consistent across subgroups, including the determinants of coronary risk discussed in the previous paragraph.Because of the constant reduction in relative risk across subgroups (that is, we are confident that there is no subgroup effect for the relative effect measure), we can infer a reduction in absolute risk of major coronary events by 1.5% (from 5% to 3.5%) in the first patient and 14.6% (from 50% to 35.4%) in the second patient.Subgroup analyses in randomised controlled trials (RCTs) or in meta-analyses of RCTs examine whether treatment effects vary according to patient group, way of giving an intervention, or approach to measuring an outcome.
Debates about subgroup effects may be framed in terms of absolute acceptance or rejection.
An approach that is more productive and more realistic is to place the likelihood that a subgroup effect is real on a continuum from “highly plausible” to “extremely unlikely”, possibly by using a visual analogue scale.