Making Sense of Hierarchical Composites

In this episode of "In the Interim…", Dr. Scott Berry is joined by statisticians Dr. Amy Crawford, Dr. Cora Allen-Savietta, and Dr. Jessica Overbey for a technical deep dive into hierarchical composite endpoints and the win ratio in clinical trial design. The group addresses clinical and statistical justifications for layered endpoint structures, demonstrates the mechanics of pairwise win ratio analysis, and explores operational and interpretive consequences in both conventional and adaptive trials. The panel scrutinizes analytic limitations, regulatory concerns, and emerging modeling strategies—all grounded in real-world trial examples.

Key Highlights

• Precise definition and use case for hierarchical composite endpoints in cardiovascular and related trials.
• Stepwise breakdown of win ratio mechanics, tie-handling, and the distinction between effect estimation (win ratio) and hypothesis testing (FS-test).
• Discussion of endpoint prevalence and dominance, risk of clinical interpretation being tied to lower-order outcomes, the role of patient exposure, and methods to parse component contributions.
• Overview of statistical power, role of simulation, and comparative advantages over other composite approaches.
• Identification of core limitations: interpretive complexity, opaque weighting, and mutable meaning of wins with maturing data.
• Review of predictive probability for adaptive interim analysis and modeling using ordinal regression.
• Opinions of US and European regulatory perspectives including support, reservations, and expectations for transparency with graphics and complementary analyses.

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