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A significant result in one subgroup and a nonsignificant result in another do not by themselves establish different treatment effects. Subgroup interpretation should consider estimates, precision and the direct interaction question together rather than comparing separate significance labels.
Define the question and assumptions
State whether the subgroup was prespecified and explain its scientific rationale. Show differences in group size, event counts and measurement conditions. The statistician should select an appropriate assessment of effect modification; comparing separate p-values does not directly test whether treatment effects differ between the subgroups.
Hypothetical study example
In a hypothetical study, the large subgroup has a narrow interval and the small subgroup a wide interval. Point estimates are similar, but only the larger group reaches statistical significance. The team avoids claiming an effect only in that group and instead presents interaction evidence and uncertainty.
Design review checks
- Record when subgroup definitions and any numerical cutoffs were selected.
- Show participant and event counts alongside estimates for each subgroup.
- Discuss false-discovery concerns when many subgroup comparisons have been examined.
Record the decision and its limits
Accepted interpretation does not use biological plausibility to retrofit explanations to attractive results. Interaction assessments may have limited power; lack of evidence is not proof of equality. Exploratory differences may generate future hypotheses but should not automatically become individual treatment recommendations or claims of established subgroup-specific benefit.
Frequently asked questions
- Does significance in only one subgroup establish a difference?
- No. Separate significance labels do not directly establish effect modification.
- Is every prespecified subgroup reliable?
- Prespecification helps, but sample size, multiplicity and uncertainty still matter.