报告题目：A Win Ratio Approach to Comparing Ordinal or Non-Normal Outcomes in Clinical Research

报告时间：2018年5月24日，星期四，上午8:30 - 11:30

报告地点：中2楼- 1242

报告人：Wang Duolao教授,  Liverpool School of Tropical Medicine,

                  The University of Liverpool, UK

报告摘要：

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. However, such a shift parameter does not have a straightforward interpretation and its confidence interval contains zero in some cases when Mann-Whitney test produces a significant result. To overcome the above 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 datasets 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.  

报告人简介：

王多劳教授，现任英国利物浦热带医学院生物统计教授，生物统计组主任、热带病临床研究中心副主任、Lancet评审、BMJ Open Heart统计副编辑，英国皇家统计学会会员。王多劳教授在临床试验设计、妇幼健康评价及数理统计方面具有深厚造诣，研究兴趣广泛，涉及统计理论方法、贝叶斯统计、妇幼健康、临床试验、流行性方法及转化医学等多个方面，先后主持多个大型研究项目的设计、分析工作，成果斐然，饱受赞誉，已先后在NEJM, Lancet，JAMA等著名期刊发表文章一百余篇, 出版专著十余部。