Plan subgroup analyses by prespecifying a small set of subgroups with a stated rationale, testing subgroup-by-treatment interaction, and recording the plan before analysis. The interaction test is the key safeguard: it asks whether the treatment effect differs between subgroups, which is a different question from whether the effect is significant inside each subgroup.
Build the plan in this order
- Name each subgroup and its rationale. Limit the set to a small prespecified number and state the clinical or biological reason the effect might differ across those groups. A rationale tied to mechanism or prior evidence keeps the list from expanding to every variable in the dataset.
- Specify the interaction test and the effect measure. State that you will test subgroup-by-treatment interaction, and name the effect measure used for the pooled estimate, consistent with the outcome's scale.
- Set the interpretation rule before seeing results. Decide in advance that a difference in effect across subgroups is claimed only when the interaction test supports it, and that a significant result within one subgroup alone will be reported as a within-subgroup finding.
- Record the plan with a date before analysis begins, so the prespecified set and the interaction test can be distinguished from later exploratory comparisons.
- Report every prespecified subgroup, including those with no evidence of interaction, and label any additional subgroup comparison as exploratory.
Why this controls false positives
Each additional subgroup comparison adds another chance of a spurious significant result. Restricting the set to a few prespecified subgroups with a rationale limits the number of tests, and the interaction test gives a single check on whether the effect actually differs between groups. Cochrane's meta-analysis guidance covers effect measures, heterogeneity, and careful interpretation of pooled results, which are the same elements a subgroup plan depends on. GRADE's approach to rating certainty of evidence works at the outcome level with explicit criteria, which helps when you decide how much confidence a subgroup finding deserves.
Common failure modes
- Scanning many baseline variables for a significant interaction and reporting only the ones that reach significance.
- Treating a significant effect in one subgroup and a nonsignificant effect in another as proof that the subgroups differ.
- Adding subgroups after seeing the data without labeling them exploratory.
The deliverable is a short written plan that names the prespecified subgroups, their rationale, the interaction test, and the rule for interpreting a positive interaction result.