Large sample inference for a win ratio analysis of a composite outcome based on prioritized components
Large sample inference for a win ratio analysis of a composite outcome based on prioritized components
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DOI：
10.1093/biostatistics/kxv032
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发表时间：
2016-01-01
期刊：
影响因子：
2.1
通讯作者：
Lachin, John M.
中科院分区：
文献类型：
--
作者：
Bebu, Ionut;Lachin, John M.
复合结果在临床试验中是常见的，特别是对于多个事件发生时间结果(终点)。将时间用于第一个结果事件的标准方法具有重要的局限性。已经提出了几种替代方法来比较治疗和对照，包括支持治疗的比例和胜率。在这里，我们使用某些多变量多样本U-统计量的大样本分布，基于按优先顺序排列的分量，在复合结果的背景下构造显著性检验和可信区间检验。这种非参数方法提供了有利于治疗的比例和胜率的一般推断，并可推广到分层分析和两组以上的比较。所提出的方法以临床试验的事件发生时间结果数据为例进行了说明。
Composite outcomes are common in clinical trials, especially for multiple time-to-event outcomes (endpoints). The standard approach that uses the time to the first outcome event has important limitations. Several alternative approaches have been proposed to compare treatment versus control, including the proportion in favor of treatment and the win ratio. Herein, we construct tests of significance and confidence intervals in the context of composite outcomes based on prioritized components using the large sample distribution of certain multivariate multi-sample U-statistics. This non-parametric approach provides a general inference for both the proportion in favor of treatment and the win ratio, and can be extended to stratified analyses and the comparison of more than two groups. The proposed methods are illustrated with time-to-event outcomes data from a clinical trial.