Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Clinical Futures, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada; Robarts Research Institute, Western University, London, ON, Canada.
Contemp Clin Trials. 2024 Nov;146:107665. doi: 10.1016/j.cct.2024.107665. Epub 2024 Aug 22.
Randomized controlled trials commonly employ multiple endpoints to collectively assess the intended effects of the new intervention on multiple aspects of the disease. Focusing on the estimation of the global win probability (WinP), defined as the (weighted) mean of the WinPs across the endpoints that a treated participant would have a better outcome than a control participant, we propose a closed-form sample size formula incorporating pre-specified precision and assurance, with precision denoted by the lower limit of confidence interval and assurance denoted by the probability of achieving that lower limit. We make use of the equivalence of the WinP and the area under the receiver operating characteristic curve (AUC) and adapt a formula originally developed for the difference between two AUCs to handle the global WinP. Unequal variances between treatment groups are allowed. Simulation results suggest that the method performs very well. We illustrate the proposed formula using a Parkinson's disease clinical trial design example.
随机对照试验通常采用多个终点来综合评估新干预措施对疾病多个方面的预期效果。本文聚焦于全球赢率（WinP）的估计，该指标定义为（加权）治疗组患者在所有终点上比对照组患者有更好结局的概率。我们提出了一个包含预先指定的精度和保证的封闭形式的样本量公式，其中精度用置信区间的下限表示，保证用实现该下限的概率表示。我们利用 WinP 与受试者工作特征曲线下面积（AUC）的等价性，并采用最初为处理两个 AUC 差异而开发的公式来处理全局 WinP。允许处理组之间存在方差不等。模拟结果表明，该方法表现非常好。我们使用帕金森病临床试验设计示例来说明所提出的公式。
