Dong Gaohong, Cui Ying, Gamalo-Siebers Margaret, Liao Ran, Liu Dacheng, Hoaglin David C, Lu Ying
BeiGene, Ridgefield Park, New Jersey, USA.
Department of Biomedical Data Science, Stanford University, Stanford, California, USA.
J Biopharm Stat. 2025 May;35(3):457-464. doi: 10.1080/10543406.2024.2374857. Epub 2024 Oct 8.
Dong et al. (2023) showed that the win statistics (win ratio, win odds, and net benefit) can complement each another to demonstrate the strength of treatment effects in randomized trials with prioritized multiple outcomes. This result was built on the connections among the point and variance estimates of the three statistics, and the approximate equality of Z-values in their statistical tests. However, the impact of this approximation was not clear. This Discussion refines this approach and shows that the approximate equality of Z-values for the win statistics holds more generally. Thus, the three win statistics consistently yield closely similar p-values. In addition, our simulations show an example that the naive approach without adjustment for censoring bias may produce a completely opposite conclusion from the true results, whereas the IPCW (inverse-probability-of-censoring weighting) approach can effectively adjust the win statistics to the corresponding true values (i.e. IPCW-adjusted win statistics are unbiased estimators of treatment effect).
董等人（2023年）表明，获胜统计量（胜率、获胜几率和净效益）可以相互补充，以在具有优先多个结局的随机试验中证明治疗效果的强度。这一结果建立在这三个统计量的点估计和方差估计之间的联系以及它们统计检验中Z值的近似相等的基础上。然而，这种近似的影响尚不清楚。本讨论细化了这种方法，并表明获胜统计量的Z值近似相等更普遍成立。因此，这三个获胜统计量始终产生非常相似的p值。此外，我们的模拟给出了一个例子，即未调整删失偏倚的简单方法可能会得出与真实结果完全相反的结论，而逆概率删失加权（IPCW）方法可以有效地将获胜统计量调整到相应的真实值（即IPCW调整后的获胜统计量是治疗效果的无偏估计量）。
