General Pairwise Comparison (GPC) statistics, such as the net benefit and the win ratio, have been applied in clinical trial data analysis and design. In the literature, inferential methods based on re-sampling, asymptotic or exact methods have been proposed for these GPC statistics, but they have not been compared to each other. In this paper, the small sample bias of the variance estimation, Type I error control and 95% confidence interval coverage of the GPC inferential methods are evaluated using simulations. The exact permutation and bootstrap tests perform best in all evaluated aspects for the net benefit, while the exact bootstrap test performs best for the win ratio.

一般成对比较 (GPC) 统计数据，例如净收益和胜出率，已应用于临床试验数据分析和设计。在文献中，已经针对这些 GPC 统计提出了基于重采样、渐近或精确方法的推理方法，但尚未将它们相互比较。在本文中，使用模拟评估了方差估计的小样本偏差、I 类误差控制和 GPC 推理方法的 95% 置信区间覆盖率。精确置换和引导测试在所有评估方面的净收益表现最佳，而精确引导测试在赢率方面表现最佳。