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Powering a trial to detect subgroup-by-treatment interactions is, in most realistic scenarios, simply not feasible. At the design stage, investigators presume clinical equipoise, that is they do not expect treatment to harm any group of patients. The overall effect is the weighted average of subgroup-specific effects and the expectation of clinical equipoise sharply constrains how different those effects can be. Under these assumptions, the sample sizes required to detect interactions are dramatically larger than those needed to detect the overall treatment effect. In a trial designed for 80% power on the overall effect, detecting an interaction of comparable magnitude would require at least four times the sample size, and even under ideal conditions, interaction test power hovers around 29%. However, power to detect a subgroup-specific effect is not the same as power to detect an interaction, and these reflect fundamentally different scientific goals. Typically, a patient does not care whether their treatment effect is statistically distinguishable from another subgroup's, they care whether there is a clinically and statistically meaningful benefit within their own subgroup. Meaningful, valid estimates of subgroup-specific effects are achievable without relying on formal interaction tests, and pre-specifying subgroups and reporting within-subgroup estimates directly is both statistically sound and more aligned with how patients and clinicians actually use trial results.

October 6, 2026

11:00 AM – 12:00 PM

IDWSDS 2026