DSI, I-Biostat, University Hasselt , Hasselt, Belgium.
Neurobiology Research Unit, Rigshospitalet and University of Copenhagen , Copenhagen, Denmark.
J Biopharm Stat. 2020 Sep 2;30(5):765-782. doi: 10.1080/10543406.2020.1730873. Epub 2020 Feb 25.
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）统计量，如净效益和胜率，已被应用于临床试验数据分析和设计。在文献中，已经提出了基于重抽样、渐近或精确方法的这些 GPC 统计量的推断方法，但尚未对它们进行相互比较。在本文中，使用模拟评估了 GPC 推断方法的方差估计的小样本偏差、I 类错误控制和 95%置信区间覆盖率。对于净效益，精确置换和引导检验在所有评估方面表现最好，而对于胜率，精确引导检验表现最好。
