In clinical trials, some patient subgroups are likely to demonstrate larger effect sizes than other subgroups. For example, the effect size, or informally the benefit with treatment, is often greater in patients with a moderate condition of a disease than in those with a mild condition. A limitation of the usual method of analysis is that it does not incorporate this ordering of effect size by patient subgroup. We propose a test statistic which supplements the conventional test by including this information and simultaneously tests the null hypothesis in pre-specified subgroups and in the overall sample. It results in more power than the conventional test when the differences in effect sizes across subgroups are at least moderately large; otherwise it loses power. The method involves combining p-values from models fit to pre-specified subgroups and the overall sample in a manner that assigns greater weight to subgroups in which a larger effect size is expected. Results are presented for randomized trials with two and three subgroups.
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