Subgroup analysis: application to individual patient decisions.

CLINICAL TRIALS PROVIDE EVIDENCE of effectiveness of treatments as an average for a group of patients, yet, in clinical medicine, we usually wish to apply these results to individuals. Can we simply apply the overall trial result for each patient, or can the result be tailored to individual patients in some way? Consider a hypothetical example: a randomised trial comparing treatments A and B shows that treatment A is more effective than B among men (P < 0.001), but not among women (not signficant). Does this mean men should receive the new treatment, but women should not? Box 1 illustrates these results from three different studies. In Study 1, the estimated treatment effect in men and women is the same — a 25% reduction in mortality associated with treatment A — but the much smaller number of women in the study gives rise to wider confidence intervals for this subgroup. In this case, there is no basis to consider that the treatment is any less effective in women (no heterogeneity; ie, non-significant test for interaction). Treatment could be considered effective for any patient regardless of sex. In Study 2, the treatment effect in women is less than that in men (8% v 25% relative reduction, or 0.92 v 0.75 relative risk, respectively), but the effects in both are still consistent with the overall result of a 20% relative reduction, and there is no evidence of significant heterogeneity between groups (test for interaction, P = 0.20). Here, the different results between men and women could be simply due to chance, and it would still be appropriate to apply the overall estimate to both men and women (unless there was additional evidence). 1 In Study 3, the observed effects for men and women are sufficiently different to suggest that this difference is unlikely to be due to chance (test for interaction, P = 0.01), and it is reasonable to conclude that the treatment effect differs between men and women. In this instance, the trial evidence should be considered separately for these subgroups. However , even here, a test of interaction can still give a low P value simply on the play of chance if many subgroups have been evaluated. 2 A practical approach Consider applying the overall trial treatment effect to each subgroup How should we decide in practice whether to consider the treatment effects for these subgroups separately? A practical approach …