用于以保证度和精度估计全局胜率所需的样本量规划
Sample size planning for estimating the global win probability with assurance and precision
AI总结：
针对多终点临床试验中全局胜率的估计问题，本文提出一个闭式样本量公式，同时纳入预先指定的精度和保证度，并利用 WinP 与 AUC 的等价性进行推导，模拟和帕金森病试验示例验证了方法表现良好。
摘要：
大多数药物开发中开展的临床试验包含多个终点，以综合评估药物对各种疾病特征的预期效果。围绕全局胜率的估计，全局胜率定义为各终点上接受治疗的参与者比对照参与者结局更好的平均胜率（WinP），我们提出了一个包含预先指定精度和保证度的闭式样本量公式，其中精度用置信区间下限表示，保证度用达到该下限的概率表示。我们利用 WinP 与受试者工作特征曲线下面积（AUC）之间的等价性，并对一个最初为两个 AUC 之差开发的公式进行调整，以处理全局 WinP。允许方差不相等。模拟结果表明该方法表现非常好。我们使用一个帕金森病临床试验设计示例来说明所提出的公式。
英文摘要：
Most clinical trials conducted in drug development contain multiple endpoints in order to collectively assess the intended effects of the drug on various disease characteristics. Focusing on the estimation of the global win probability, defined as the average win probability (WinP) across 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 variance is allowed. Simulation results suggest that the method performs very well. We illustrate the proposed formula using a Parkinson's disease clinical trial design example.
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