Wang Duolao, Pocock Stuart
Biostatistics Unit, Department of Clinical Sciences, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK.
Department of Medical Statistics, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
Pharm Stat. 2016 May;15(3):238-45. doi: 10.1002/pst.1743. Epub 2016 Mar 11.
Clinical trials are often designed to compare continuous non-normal outcomes. The conventional statistical method for such a comparison is a non-parametric Mann-Whitney test, which provides a P-value for testing the hypothesis that the distributions of both treatment groups are identical, but does not provide a simple and straightforward estimate of treatment effect. For that, Hodges and Lehmann proposed estimating the shift parameter between two populations and its confidence interval (CI). However, such a shift parameter does not have a straightforward interpretation, and its CI contains zero in some cases when Mann-Whitney test produces a significant result. To overcome the aforementioned problems, we introduce the use of the win ratio for analysing such data. Patients in the new and control treatment are formed into all possible pairs. For each pair, the new treatment patient is labelled a 'winner' or a 'loser' if it is known who had the more favourable outcome. The win ratio is the total number of winners divided by the total numbers of losers. A 95% CI for the win ratio can be obtained using the bootstrap method. Statistical properties of the win ratio statistic are investigated using two real trial data sets and six simulation studies. Results show that the win ratio method has about the same power as the Mann-Whitney method. We recommend the use of the win ratio method for estimating the treatment effect (and CI) and the Mann-Whitney method for calculating the P-value for comparing continuous non-Normal outcomes when the amount of tied pairs is small. Copyright © 2016 John Wiley & Sons, Ltd.
临床试验通常旨在比较连续的非正态结果。用于此类比较的传统统计方法是非参数曼-惠特尼检验，它提供一个P值来检验两个治疗组分布相同的假设，但没有提供一个简单直接的治疗效果估计值。为此，霍奇斯和莱曼提出估计两个总体之间的偏移参数及其置信区间（CI）。然而，这样的偏移参数没有直接的解释，并且当曼-惠特尼检验产生显著结果时，其置信区间在某些情况下会包含零。为了克服上述问题，我们引入使用胜率来分析此类数据。新治疗组和对照组的患者组成所有可能的配对。对于每一对，如果知道谁的结果更有利，新治疗组的患者就被标记为“胜者”或“败者”。胜率是胜者总数除以败者总数。可以使用自助法获得胜率的95%置信区间。使用两个真实试验数据集和六个模拟研究来研究胜率统计量的统计特性。结果表明，胜率法的检验效能与曼-惠特尼法大致相同。我们建议，当平局配对数量较少时，使用胜率法来估计治疗效果（和置信区间），使用曼-惠特尼法来计算比较连续非正态结果的P值。版权所有© 2016约翰·威利父子有限公司。
