首页 | 本学科首页   官方微博 | 高级检索  
     


Win statistics (win ratio,win odds,and net benefit) can complement one another to show the strength of the treatment effect on time-to-event outcomes
Authors:Gaohong Dong  Bo Huang  Johan Verbeeck  Ying Cui  James Song  Margaret Gamalo-Siebers  Duolao Wang  David C. Hoaglin  Yodit Seifu  Tobias Mütze  John Kolassa
Affiliation:1. BeiGene, Ridgefield Park, New Jersey, USA;2. Pfizer Inc., Groton, Connecticut, USA;3. DSI, I-Biostat, University Hasselt, Hasselt, Belgium;4. Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USA;5. Pfizer Inc., Collegeville, Pennsylvania, USA;6. Liverpool School of Tropical Medicine, Liverpool, UK;7. Department of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, Massachusetts, USA;8. Bristol Myers Squibb, Berkeley Heights, New Jersey, USA;9. Statistical Methodology, Novartis Pharma AG, Basel, Switzerland;10. Department of Statistics, Rutgers University, Piscataway, New Jersey, USA
Abstract:
Conventional analyses of a composite of multiple time-to-event outcomes use the time to the first event. However, the first event may not be the most important outcome. To address this limitation, generalized pairwise comparisons and win statistics (win ratio, win odds, and net benefit) have become popular and have been applied to clinical trial practice. However, win ratio, win odds, and net benefit have typically been used separately. In this article, we examine the use of these three win statistics jointly for time-to-event outcomes. First, we explain the relation of point estimates and variances among the three win statistics, and the relation between the net benefit and the Mann–Whitney U statistic. Then we explain that the three win statistics are based on the same win proportions, and they test the same null hypothesis of equal win probabilities in two groups. We show theoretically that the Z-values of the corresponding statistical tests are approximately equal; therefore, the three win statistics provide very similar p-values and statistical powers. Finally, using simulation studies and data from a clinical trial, we demonstrate that, when there is no (or little) censoring, the three win statistics can complement one another to show the strength of the treatment effect. However, when the amount of censoring is not small, and without adjustment for censoring, the win odds and the net benefit may have an advantage for interpreting the treatment effect; with adjustment (e.g., IPCW adjustment) for censoring, the three win statistics can complement one another to show the strength of the treatment effect. For calculations we use the R package WINS, available on the CRAN (Comprehensive R Archive Network).
Keywords:IPCW  IPCW-adjusted win statistics  inverse-probability-of-censoring weighting  generalized pairwise comparisons  Mann–Whitney U statistic
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号