arXiv subjects

Yunhan Mou

Publications and source records attributed to Yunhan Mou.

4 recordsLinked to original sources

arXiv2025

Integrating Prioritized and Non-Prioritized Structures in Win Statistics

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.

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arXiv2025

Correlation Matters! Streamlining the Sample Size Procedure with Composite Time-to-event Endpoints

Composite endpoints are widely used in cardiovascular clinical trials to improve statistical efficiency while preserving clinical relevance. The Win Ratio (WR) measure and more general frameworks of Win Statistics have emerged as increasingly popular alternatives to traditional time-to-first-event analyses. Although analytic sample size formulas for WR have been developed, they rely on design parameters that are often not straightforward to specify. Consequently, sample size determination in clinical trials with WR as the primary analysis is most often based on simulations, which can be computationally intensive. Moreover, these simulations commonly assume independence among component endpoints, an assumption that may not hold in practice and can lead to misleading power estimates. To address this challenge, we derive refined formulas to calculate the proportions of wins, losses, and ties for multiple prioritized time-to-event endpoints. These formulas rely on familiar design inputs and become directly applicable when integrated with existing sample size methods. We conduct a comprehensive assessment of how correlation among endpoints affects sample size requirements across varying design features. We further demonstrate the role of correlations through two case studies based on the landmark SPRINT and STICH clinical trials to generate further insights.

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arXiv2025

Testing Prioritized Composite Endpoint with Multiple Follow-up Time Examinations

Composite endpoints are widely used in cardiovascular clinical trials. In recent years, hierarchical composite endpoints-particularly the win ratio approach and its predecessor, the Finkelstein-Schoenfeld (FS) test, also known as the unmatched win ratio test-have gained popularity. These methods involve comparing individuals across multiple endpoints, ranked by priority, with mortality typically assigned the highest priority in many applications. However, these methods have not accounted for varying treatment effects, known as non-constant hazards over time in the context of survival analysis. To address this limitation, we propose an adaptation of the FS test that incorporates progressive follow-up time, which we will refer to as ProFS. This proposed test can jointly evaluate treatment effects at various follow-up time points by incorporating the maximum of several FS test statistics calculated at those specific times. Moreover, ProFS also supports clinical trials with group sequential monitoring strategies, providing flexibility in trial design. As demonstrated through extensive simulations, ProFS offers increased statistical power in scenarios where the treatment effect is mainly in the short term or when the second (non-fatal) layer might be concealed by a lack of effect or weak effect on the top (fatal) layer. We also apply ProFS to the SPRINT clinical trial, illustrating how our proposed method improves the performance of FS.

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arXiv2024

Generalizing the Finkelstein-Schoenfeld Test to Incorporate Multiple Alternating Thresholds

Composite endpoints consisting of both terminal and non-terminal events, such as death and hospitalization, are frequently used in cardiovascular clinical trials. The Finkelstein-Schoenfeld (FS) test provides a way to employ a hierarchical structure to combine fatal and non-fatal events by giving death information an absolute priority, which may limit the contribution of clinically meaningful non-fatal events. To provide a more flexible alternative, we propose the Finkelstein-Schoenfeld with Multiple Thresholds (FS-MT) test, which extends the standard FS test by incorporating multiple thresholds applied sequentially and alternating across endpoints. A weighted adaptive approach is also developed to help determine the thresholds in FS-MT. The proposed approach retains the statistical properties of the FS test while allowing more flexible use of information from lower-priority events. We evaluate the operating characteristics of the proposed test through simulations that vary the follow-up time, the correlation between events, and the treatment effect sizes. A case study based on the Digitalis Investigation Group clinical trial data is presented to further illustrate our proposed method. An R package ``FSMT'' that implements the proposed methodology has been developed.

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