BeiGene, Ridgefield Park, New Jersey, USA.
Pfizer Inc., Groton, Connecticut, USA.
Pharm Stat. 2023 Jan;22(1):20-33. doi: 10.1002/pst.2251. Epub 2022 Jun 27.
Conventional analyses of a composite of multiple time-to-event outcomes use the time to the first event. However, the first event may not be the most important outcome. To address this limitation, generalized pairwise comparisons and win statistics (win ratio, win odds, and net benefit) have become popular and have been applied to clinical trial practice. However, win ratio, win odds, and net benefit have typically been used separately. In this article, we examine the use of these three win statistics jointly for time-to-event outcomes. First, we explain the relation of point estimates and variances among the three win statistics, and the relation between the net benefit and the Mann-Whitney U statistic. Then we explain that the three win statistics are based on the same win proportions, and they test the same null hypothesis of equal win probabilities in two groups. We show theoretically that the Z-values of the corresponding statistical tests are approximately equal; therefore, the three win statistics provide very similar p-values and statistical powers. Finally, using simulation studies and data from a clinical trial, we demonstrate that, when there is no (or little) censoring, the three win statistics can complement one another to show the strength of the treatment effect. However, when the amount of censoring is not small, and without adjustment for censoring, the win odds and the net benefit may have an advantage for interpreting the treatment effect; with adjustment (e.g., IPCW adjustment) for censoring, the three win statistics can complement one another to show the strength of the treatment effect. For calculations we use the R package WINS, available on the CRAN (Comprehensive R Archive Network).
传统的复合多个生存时间结局的分析使用第一次事件的时间。然而，第一次事件可能不是最重要的结果。为了解决这个局限性，广义成对比较和赢率统计（赢率、赢赔率和净效益）变得流行起来，并已应用于临床试验实践。然而，赢率、赢赔率和净效益通常是分开使用的。在本文中，我们研究了这三种赢率统计数据在生存时间结局中的联合使用。首先，我们解释了这三种赢率统计数据的点估计和方差之间的关系，以及净效益与曼-惠特尼 U 统计量之间的关系。然后，我们解释了这三种赢率统计数据是基于相同的赢率，它们检验两组中赢概率相等的零假设。我们从理论上表明，相应统计检验的 Z 值近似相等；因此，这三种赢率统计数据提供非常相似的 P 值和统计功效。最后，通过模拟研究和临床试验数据，我们表明，当没有（或很少）删失时，这三种赢率统计数据可以相互补充，以显示治疗效果的强度。然而，当删失量不小，并且没有对删失进行调整时，赢赔率和净效益可能更有利于解释治疗效果；对删失进行调整（例如，IPCW 调整）后，这三种赢率统计数据可以相互补充，以显示治疗效果的强度。计算使用 R 包 WINS，可在 CRAN（综合 R 存档网络）上获得。
