[Win ratio, a new technique for the analysis of multiple events in clinical trials].

IF 0.7 Q4 CARDIAC & CARDIOVASCULAR SYSTEMS
Paolo Verdecchia, Fabio Angeli, Gianpaolo Reboldi
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引用次数: 0

Abstract

The win ratio is a relatively recent statistical technique introduced with the aim of better managing the analysis of clinical trials involving more than one clinical event as an endpoint in the evaluation of a treatment. The calculation of the win ratio begins by defining a "hierarchy" of clinical events on the basis of their severity, e.g. death, followed, for example, by the number of hospitalizations for heart failure, followed, for example, by softer endpoints including functional or laboratory changes. The analysis begins by comparing each patient in a hypothetical "Group A" with each patient in a hypothetical "Group B" on the hierarchically most important clinical event only. If the patient in Group B dies and the one in Group A does not, or if the one in Group B dies before the one in Group A, then that particular comparison is "won" by Group A. If the patient in Group A dies and the one in Group B does not, or if the one in Group A dies before the one in Group B, then that comparison is "won" by Group B. If nobody dies, or if they die on the same day, then that specific comparison ends in a tie. All comparisons of each patient in Group A with each patient in Group B are then performed. On the comparisons that end in a tie, we move on to the analysis of the endpoint hierarchically in second place, using the same technique. Then we proceed to the analysis of the endpoint hierarchically in third place, and so on down to the event in the lowest position in the hierarchy. The win ratio represents the total sum of comparisons in which, for example, Group A wins, divided by the total sum of comparisons in which Group A "loses". The absolute difference, rather than the ratio, between these two sums indicates the "win difference". Compared to the conventional "time-to-event" statistical techniques including, for example, the Cox model, the win ratio calculation has advantages, but also potential disadvantages. This review aims to offer a summary of the advantages and potential disadvantages of the win ratio, with some practical examples derived from the use of the win ratio in the analysis of important trials in the cardiovascular area.

[胜率,临床试验中多事件分析的新技术]。
胜率是一种相对较新的统计技术,其目的是更好地管理临床试验的分析,这些临床试验涉及多个临床事件,作为治疗评估的终点。胜率的计算首先根据严重程度确定临床事件的“等级”,例如死亡,其次是因心力衰竭住院的次数,然后是较软的终点,例如功能或实验室变化。分析首先比较假设的“a组”和假设的“B组”的每个患者,仅在等级上最重要的临床事件上。如果病人在B组死亡和一组不,或者一个在前B组死亡一个组,然后那个比较“赢得”集团在A组答:如果病人死了,在B组不,或者一个在B组,前组死亡则是“赢得”组比较B。如果没有人死亡,或者如果他们死在同一天,那么具体的比较以平局收场。然后对A组和B组的所有患者进行比较。在以平局结束的比较中,我们使用相同的技术,继续对第二层次的端点进行分析。然后,我们继续对第三个位置的端点进行层次结构分析,依此类推,直至层次结构中最低位置的事件。胜率表示,例如,A组获胜的比较总数除以A组“输”的比较总数。这两个总和之间的绝对差异,而不是比率,表示“赢差”。与传统的“事件时间”统计技术(例如Cox模型)相比,胜率计算有优点,但也有潜在的缺点。这篇综述的目的是总结胜率的优点和潜在的缺点,并给出一些实际的例子,这些例子来自于在心血管领域的重要试验分析中使用胜率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Giornale italiano di cardiologia
Giornale italiano di cardiologia CARDIAC & CARDIOVASCULAR SYSTEMS-
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