Rauch Geraldine, Kieser M
Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany.
Methods Inf Med. 2012;51(4):309-17. doi: 10.3414/ME11-01-0044. Epub 2012 Apr 24.
Binary composite outcome measures are increasingly used as primary endpoints in clinical trials. Composite endpoints combine several events of interest within a single variable. However, as the effect observed for the composite does not necessarily reflect the effects for the individual components, it is recommended in the literature to additionally evaluate each component separately.
The task is to define an adequate multiple test procedure which focuses on the composite outcome measure but allows for a confirmatory interpretation of the components in case of large effects.
In this paper, we determine the correlation matrix for a multiple binary endpoint problem of a composite endpoint and its components based on the normal approximation test statistic for rates. Thereby, we assume multinomial distributed components. We use this correlation to calculate the adjusted local significance levels. We discuss how to use our approach for a more informative formulation of the test problem. Our work is illustrated by two clinical trial examples.
By taking into account the special correlation structure between a binary composite outcome and its components, an adequate multiple test procedure to assess the composite and its components can be defined based on an approximate multivariate normal distribution without much loss in power compared to a test problem formulated exclusively for the composite.
By incorporating the correlation under the null hypotheses, the global power for the multiple test problem assessing both the composite and its components can be increased as compared to simple Bonferroni-adjustment. Thus, a confirmatory analysis of the composite and its components might be possible without a large increase in sample size as compared to a single endpoint problem formulated exclusively for the composite.
二元复合结局指标在临床试验中越来越多地被用作主要终点。复合终点将多个感兴趣的事件合并在一个单一变量中。然而，由于观察到的复合效应不一定反映各个组成部分的效应，文献中建议额外分别评估每个组成部分。
任务是定义一种适当的多重检验程序，该程序侧重于复合结局指标，但在效应较大时允许对各组成部分进行验证性解释。
在本文中，我们基于率的正态近似检验统计量，确定复合终点及其组成部分的多重二元终点问题的相关矩阵。因此，我们假设各组成部分服从多项分布。我们使用这种相关性来计算调整后的局部显著性水平。我们讨论如何使用我们的方法对检验问题进行更具信息量的表述。我们的工作通过两个临床试验实例进行说明。
通过考虑二元复合结局与其组成部分之间的特殊相关结构，可以基于近似多元正态分布定义一种适当的多重检验程序，以评估复合结局及其组成部分，与专门针对复合结局制定的检验问题相比，功效损失不大。
通过在原假设下纳入相关性，与简单的Bonferroni调整相比，评估复合结局及其组成部分的多重检验问题的总体功效可以提高。因此，与专门针对复合结局制定的单一终点问题相比，在不大幅增加样本量的情况下，对复合结局及其组成部分进行验证性分析可能是可行的。
