On recurrent-event win ratio.
On recurrent-event win ratio.
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DOI：
10.1177/09622802221084134
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发表时间：
2022-06
影响因子：
2.3
通讯作者：
中科院分区：
文献类型：
--
作者：
Pocock等人（2012）提出的胜率方法已成为分析死亡和非致死性事件（如住院）复合终点的常用工具。然而，其标准版本仅通过第一次发生来借鉴非致命性事件。为了统计效率和临床可解释性，我们构建并比较了更充分利用复发事件的不同赢率变体。我们特别注意一个称为最后事件辅助胜率的变量，它比较了两个患者的非致命事件的累积频率，并在最近一次事件发生时打破了联系。结果表明，最后一个事件辅助的胜利比使用更多的数据比标准的胜利比，但减少到后者时，非致命事件发生最多一次。我们进一步证明，最后一个事件辅助的胜利比拒绝零假设的大概率，如果治疗随机延迟所有事件。在真实环境下的仿真表明，最后一个事件辅助的胜率测试始终享有比标准胜率和其他竞争对手更高的权力。对一项真实的心血管试验的分析进一步证明了最后一个事件辅助的胜率的实际优势。最后，我们讨论了未来的工作，开发有意义的效果大小估计的基础上扩展的比较规则。建议方法的R代码包含在Comprehensive R Archive Network上公开提供的WR包中。
The win ratio approach proposed by Pocock et al. (2012) has become a popular tool for analyzing composite endpoints of death and non-fatal events like hospitalization. Its standard version, however, draws on the non-fatal event only through the first occurrence. For statistical efficiency and clinical interpretability, we construct and compare different win ratio variants that make fuller use of recurrent events. We pay special attention to a variant called last-event-assisted win ratio, which compares two patients on the cumulative frequency of the non-fatal event, with ties broken by the time of its latest episode. It is shown that last-event-assisted win ratio uses more data than the standard win ratio does but reduces to the latter when the non-fatal event occurs at most once. We further prove that last-event-assisted win ratio rejects the null hypothesis with large probability if the treatment stochastically delays all events. Simulations under realistic settings show that the last-event-assisted win ratio test consistently enjoys higher power than the standard win ratio and other competitors. Analysis of a real cardiovascular trial provides further evidence for the practical advantages of the last-event-assisted win ratio. Finally, we discuss future work to develop meaningful effect size estimands based on the extended rules of comparison. The R-code for the proposed methods is included in the package WR openly available on the Comprehensive R Archive Network.
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