Confidence interval estimation for the win probability in cluster randomized trials with hierarchical composite endpoints using win fractions.

ABSTRACT: Background/AimsCluster randomized trials with multiple endpoints feature complex correlation structures. Estimating treatment effects in a meaningful way that respects differences in scale and clinical importance is challenging. Pairwise comparison methods address these challenges by constructing all pairs featuring one treatment and one control participant, then evaluating endpoints in hierarchical order. For cluster randomized trials featuring such a "hierarchical composite endpoint," we develop large-sample confidence interval estimators and hypothesis tests for the nonparametric treatment effect referred to here as the "win probability."MethodsFor each pair of participants (one treated and one control), responses on each endpoint are compared in order of descending clinical importance

SUBMITTER: Davies Smith E 

PROVIDER: S-EPMC12931659 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature