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.
The purpose of this agreement is to develop Wisconsin's new e Government website portal.PARS has migrated to the new website portal as of Nov. 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.Subgroup analyses are common and often associated with claims of difference of treatment effects between subgroups—termed “subgroup effect”, “effect modification”, or “interaction between a subgroup variable and treatment”.1 2 3 A difference in effect between subgroups, if true, is likely to have important implications for clinical practice and policy making.By contrast with relative effects, which in most situations remain constant across varying baseline risks, absolute risk reductions will typically vary with baseline risk.
For example, consider the effect of statin therapy on major coronary events (that is, non-fatal myocardial infarction and coronary heart disease death) in patients with varying coronary risks.
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.
The question is then a decision of where on this continuum a putative subgroup effect lies.
Subgroups can be defined according to characteristics measured at baseline or after randomisation.
Subgroups defined according to post-randomisation characteristics might be influenced by tested interventions; that is, the apparent difference of treatment effect between subgroups can be explained by the intervention itself, or by differing prognostic characteristics in sub-groups that emerge after randomisation, rather than by the subgroup characteristic itself.
In 1991, Yusuf et al12 discussed principles of analysing and interpreting subgroup effects, and stated that qualitative interactions (that is, when treatment is beneficial in one subgroup but harmful in another) are rare.