Chair: Roland Matsouak, PhD (Duke University)
Speaker: Nicole (Xiaoyun) Li PhD (BeiGene USA, Inc.)
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: Bo Huang, PhD (Pfizer)
Title: Adjusted Win Statistics Using the Inverse Probability of Treatment Weighting
Abstract: The win ratio/win statistics method has been increasingly applied in the design and analysis of clinical trials. However, it is a univariate approach that does not allow for adjusting for baseline imbalances in covariates, although a stratified win ratio can be calculated when the number of strata is small. In this presentation, I will go over an adjusted win ratio/statistics method to control for such imbalances by inverse probability of treatment weighting (IPTW) method. We derive the adjusted win ratio with its variance and suggest three IPTW adjustments: IPTW-average treatment effect (IPTW-ATE), stabilized IPTW-ATE (SIPTW-ATE) and IPTW-average treatment effect in the treated (IPTW-ATT). The proposed adjusted methods are applied to analyze a composite outcome in the CHARM trial. The statistical properties of the methods are assessed through simulations. Results show that adjusted win ratio/statistics methods can correct the win ratio/win statistics for covariate imbalances at baseline. Simulation results show that the three proposed adjusted win ratios have similar power to detect the treatment difference and have slightly lower power than the corresponding adjusted Cox models when the assumption of proportional hazards holds true but have consistently higher power than adjusted Cox models when the proportional hazard assumption is violated.
Speaker: Baoshan Zhang, PhD (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.
