What Is Subgroup Analysis?

Subgroup analysis examines whether an effect differs between groups of participants or studies defined by a characteristic, such as age, sex, disease severity or intervention dose. It is useful for exploring variation but is prone to false findings, especially if done post hoc and without correcting for multiplicity.

Principles

• Pre-specify the subgroups and the expected direction of difference, with a rationale.
• Limit the number of subgroup analyses.
• Use a test for interaction to check whether effects differ between subgroups, not separate within-group p-values.
• Interpret as exploratory unless pre-specified, adequately powered and consistent with other evidence.
• Report all subgroup analyses done, not only significant ones.

A classic pitfall

Comparing "significant in one subgroup, not in the other" is misleading: the difference may be due to different sample sizes, not different effects. Only a test of interaction shows whether the effects themselves differ.

In meta-analysis

Subgroup analyses can compare studies by characteristic (for example dose or population). They are observational comparisons across studies, so confounding by other study characteristics is possible. Meta-regression extends this approach for continuous characteristics.

Illustrative example

Invented for teaching. In a trial, the treatment effect on blood pressure is significant in women (p = 0.03) and not in men (p = 0.20). The interaction test gives p = 0.45, so there is no good evidence that the effect differs between sexes. Reporting "works in women but not men" would mislead.

Practical advice for students reading papers

When a paper highlights a subgroup finding, ask four questions: Was the subgroup specified before analysis? How many subgroups were examined? Was an interaction test reported? Is the finding consistent with other studies and biologically plausible? If the answers are mostly no, treat the finding as a hypothesis for future research and not as evidence of different treatment effects.

Common pitfalls

• Slicing the data many ways until something appears.
• Basing treatment decisions on exploratory subgroups.

Key takeaways

• Pre-specify, test interactions and report all subgroups.
• Treat unplanned subgroup findings as hypotheses.

This page is general educational information about research methods. It is not medical, legal or regulatory advice; follow your institution's ethics board and your target journal's instructions.