Li Zhenyu, Izumi Aliya, Vervoort Dominique, Ranadive Anika, Verma Subodh, Fremes Stephen E
Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Schulich Heart Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
CJC Open. 2025 May 21;7(8):1097-1107. doi: 10.1016/j.cjco.2025.05.006. eCollection 2025 Aug.
The win ratio (WR), introduced in 2012, has emerged as a method to analyze hierarchical composite outcomes by prioritizing clinically significant events, unlike traditional composite time-to-event analyses, which treat events equally. However, use of the WR in biomedical research beyond cardiovascular trials remains unexplored. The study aims to investigate trends in the use of the WR in biomedical research and determine the characteristics of these articles.
Biomedical articles indexed in Web of Science and PubMed were retrieved for 2012-2024. Data extraction included bibliometric information and content details. Statistical analyses utilized descriptive statistics, correlation, and linear regression to assess publication trends and the distribution of WR methodologies across disciplines.
A total of 82 studies were analyzed. Publication counts using the WR have grown significantly since its introduction, with an annual compounded growth rate of 30.2%. Most articles were randomized controlled trials (n = 68; 82.9%). Of the 68 randomized controlled trials, 46 (67.6%) were in the field of cardiology. The unmatched WR was the predominant WR approach (n = 57; 69.5%). Mortality was the highest-ranked outcome in most studies (n = 55; 67.1%), and time-to-event variables were the most frequently used across all hierarchical outcome ranks (n = 173).
The WR has gained acceptance as a robust and clinically meaningful method for analyzing composite endpoints, particularly for cardiovascular trials. Although challenges remain, its adaptability and ability to prioritize clinically relevant outcomes make it a promising tool for future biomedical research across various disciplines.
2012年引入的胜率(WR)已成为一种通过对具有临床意义的事件进行优先排序来分析分层复合结局的方法,这与传统的复合事件发生时间分析不同,传统分析对所有事件一视同仁。然而,除心血管试验外,WR在生物医学研究中的应用尚未得到探索。本研究旨在调查生物医学研究中WR的使用趋势,并确定这些文章的特点。
检索2012年至2024年在科学网和PubMed上索引的生物医学文章。数据提取包括文献计量信息和内容细节。统计分析采用描述性统计、相关性分析和线性回归,以评估出版趋势以及WR方法在各学科中的分布情况。
共分析了82项研究。自引入以来,使用WR的文章数量显著增长,年复合增长率为30.2%。大多数文章为随机对照试验(n = 68;82.9%)。在68项随机对照试验中,46项(67.6%)属于心脏病学领域。未匹配的WR是主要的WR方法(n = 57;69.5%)。在大多数研究中,死亡率是排名最高的结局(n = 55;67.1%),并且在所有分层结局等级中,事件发生时间变量是最常用的(n = 173)。
WR已被认可为一种用于分析复合终点的可靠且具有临床意义的方法,特别是对于心血管试验。尽管仍存在挑战,但其适应性以及对临床相关结局进行优先排序的能力使其成为未来跨学科生物医学研究的一个有前景的工具。