Integrating Prioritized and Non-Prioritized Structures in Win Statistics

Yunhan Mou; Scott Hummel; Yuan Huang

doi:10.48550/arxiv.2512.18946

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Integrating Prioritized and Non-Prioritized Structures in Win Statistics

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Integrating Prioritized and Non-Prioritized Structures in Win Statistics

Yunhan Mou, Scott Hummel and Yuan Huang

ArXiv.org

Cornell University

12/22/2025

DOI: 10.48550/arxiv.2512.18946

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Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Composite endpoints are frequently used as primary or secondary analyses in cardiovascular clinical trials to increase clinical relevance and statistical efficiency. Alternatively, the Win Ratio (WR) and other Win Statistics (WS) analyses rely on a strict hierarchical ordering of endpoints, assigning higher priority to clinically important endpoints. However, determining a definitive endpoint hierarchy can be challenging and may not adequately reflect situations where endpoints have comparable importance. In this study, we discuss the challenges of endpoint prioritization, underscore its critical role in WS analyses, and propose Rotation WR (RWR), a hybrid prioritization framework that integrates both prioritized and non-prioritized structures. By permitting blocks of equally-prioritized endpoints, RWR accommodates endpoints of equal or near equal clinical importance, recurrent events, and contexts requiring individualized shared decision making. Statistical inference for RWR is developed using U-statistics theory, including the hypothesis testing procedure and confidence interval construction. Extensions to two additional WS measures, Rotation Net Benefit and Rotation Win Odds, are also provided. Through extensive simulation studies involving multiple time-to-event endpoints, including recurrent events, we demonstrate that RWR achieves valid type I error control, desirable statistical power, and accurate confidence interval coverage. We illustrate both the methodological and practical insights of our work in a case study on endpoint prioritization with the SPRINT clinical trial, highlighting its implications for real-world clinical trial studies.

Statistics - Methodology

Details

Title: Subtitle: Integrating Prioritized and Non-Prioritized Structures in Win Statistics
Creators: Yunhan Mou
Scott Hummel
Yuan Huang
Resource Type: Preprint
Publication Details: ArXiv.org
DOI: 10.48550/arxiv.2512.18946
ISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 12/22/2025
Academic Unit: Biostatistics
Record Identifier: 9985093909102771

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