Chair: Roland Matsouaka, PhD (Duke University)
Speaker: Nicole (Xiaoyun) Li, PhD (BeOne Medicine)
Title: A Win Ratio-Based Framework to Combine Multiple Clinical Endpoints in Exploratory Basket Trials
Abstract: Traditional exploratory basket trials in oncology usually only includes response rates in the quantitative Go/No-Go framework. However, the addition of information such as progression-free survival, or adverse events data may substantially improve the Go/No-Go decision metrics. In this presentation, we introduce the win-ratio framework of incorporating multiple endpoints into the quantitative Go/No-Go consideration for basket trials. We evaluate our framework using the pruning and pooling basket trial design and conduct simulations to illustrate the improvement via different scenarios. Special attentions will be provided on the practical considerations on the hierarchy of the endpoints and applications.
Speaker: Ching-Ray Yu, PhD (Pfizer)
Title: Non-parametric Statistical Methodology for Composite Endpoints in the Clinical Trials
Abstract: Finkelstein-Schoenfeld (F-S) test has been used increasingly applied in the design and analysis of clinical trials with composite endpoints. A clinical trial where F-S is used will be illustrated as an example and the summary will be provided. The hurdle of F-S approach is very computationally intensive as it evaluates the outcomes between subject-pairwise comparisons nonparametrically. We proposed a method called improved F-S (IFS) that reduces the computational time significantly. In this talk, the methodology of IFS will be introduced and simulations will be presented. In the end, a win ratio RShiny app will be introduced for the design of clinical trials with composite endpoints and sample size calculations.
Speaker: Baoshan Zhang (Duke University)
Title: Sequential Design Based on Derived Win Statistics
Abstract: The Win Ratio has gained significant traction in cardiovascular trials as a novel method for analyzing composite endpoints. Compared with conventional approaches based on time to the first event, the Win Ratio accommodates the varying priorities and types of outcomes among components, potentially offering greater statistical power by fully utilizing the information contained within each outcome. However, studies using Win Ratio have largely been confined to fixed design, limiting flexibility for early decisions, such as stopping for futility or efficacy. Our study proposes a sequential design framework incorporating multiple interim analyses based on Win Ratio or Net Benefit statistics. Moreover, we provide rigorous proof of the canonical joint distribution for sequential Win Ratio and Net Benefit statistics, and an algorithm for sample size determination is developed. We also provide results from a finite sample simulation study, which show that our proposed method controls Type I error maintains power level, and has a smaller average sample size than the fixed design. A real study of cardiovascular study is applied to illustrate the proposed method.
Speaker: Tuo Wang, PhD (Eli Lilly and Company )
Title: Improve the Precision of Area Under the Curve Estimation for Recurrent Events Through Covariate Adjustment
Abstract: The area under the curve (AUC) of the mean cumulative function (MCF) has recently been introduced as a novel estimand for evaluating treatment effects in recurrent event settings, capturing a totality of evidence in relation to disease progression. While the Lin-Wei-Yang-Ying (LWYY) model is commonly used for analyzing recurrent events, it relies on the proportional rate assumption between treatment arms, which is often violated in practice. In contrast, the AUC under MCFs does not depend on such proportionality assumptions and offers a clinically interpretable measure of treatment effect. To improve the precision of the AUC estimation while preserving its unconditional interpretability, we propose a nonparametric covariate adjustment approach. This approach guarantees efficiency gain compared to unadjusted analysis, as demonstrated by theoretical asymptotic distributions, and is universally applicable to various randomization schemes, including both simple and covariate-adaptive designs. Extensive simulations across different scenarios further support its advantage in increasing statistical power. Our findings highlight the importance of covariate adjustment for the analysis of AUC in recurrent event settings, offering practical guidance for its application in randomized clinical trials.
