In Reply Thank you for your interest in our article, “Effect of Low-Dose Supplementation of Arginine Vasopressin on Need for Blood Product Transfusions in Patients With Trauma and Hemorrhagic Shock.”¹ We believe Gauss et al have misread our statistical approach and are incorrectly interpreting the absolute standardized differences (ASD) as P values. There is a growing momentum to use P value alternatives, such as ASDs, in clinical research.^2,3 Absolute standardized differences assess the magnitude of differences between groups (specifically, the absolute value of the difference in means, mean ranks, or proportions divided by the pooled standard deviation). We used ASD rather than standard significance tests because using multiple significant tests to evaluate baseline variables can be misleading. Each test carries a 5% probability of type 1 error; with multiple statistical tests, the type I error rate becomes exaggerated, resulting in an inflated probability of a significant difference between groups based on random chance alone. The use of ASD mitigates the risk of amplifying type 1 errors. Groups were considered imbalanced for any variable that had absolute standardized differences greater than 0.392. All baseline variables had an ASD less than 0.392, suggesting that groups were not imbalanced for these variables.

在答复中感谢您对我们的文章“低剂量补充精氨酸加压素对创伤和出血性休克患者需要输血的影响”。¹我们认为Gauss等人误解了我们的统计方法，并且错误地将绝对标准差（ASD）解释为P值。在临床研究中，使用P值替代品（例如ASD）的势头越来越大。^{2 ，3-}绝对标准差评估组之间差异的大小（具体而言，均值，均值等级或比例之差的绝对值除以合并的标准偏差）。我们使用ASD而不是标准显着性检验，因为使用多个重要检验来评估基线变量可能会产生误导。每个测试的类型1错误的概率为5％；如果进行多项统计检验，则I型错误率会被夸大，从而导致仅基于随机机会而导致组间显着差异的机率就会升高。使用ASD可以减轻放大1型错误的风险。绝对标准化差大于0.392的任何变量均被视为组不平衡。所有基线变量的ASD均小于0.392，