Informative Simultaneous Confidence Intervals in Hierarchical Testing
Informative Simultaneous Confidence Intervals in Hierarchical Testing
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DOI：
10.3414/me13-01-0140
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发表时间：
2014-01-01
影响因子：
1.7
通讯作者：
Brannath, W.
中科院分区：
文献类型：
--
作者：
Schmidt, S.;Brannath, W.
背景和目标：在涉及多个测试的临床试验中，通常很难获得信息性的同时置信区间（SCI）。特别是在分层检验中，对于第一个检验的（也是最重要的）假设，在其被拒绝后，不可能量化其影响。我们的目标是一个建设的SCI，总是informations.Methods：我们提出了一种方法，在拒绝每个假设，以获得一个翔实的置信区间后，水平分裂。分裂权重是参数的连续函数。我们的方法是可实现的一个简单的算法，并说明了一个直观的图形representation.Results：我们从理论上和一个例子表明，新的SAS总是提供信息时，一个假设被拒绝。拒绝第一个假设的能力不小于经典的固定序列程序。额外的信息的价格是一个小功率损失的假设进行最重要的one.Conclusions：鉴于大量的信息增益，功率的非主要假设的小损失似乎往往是可以接受的。特别是在非劣效性试验的背景下，这种方法是一种有用的替代方法。权函数选择的灵活性使得该方法具有很好的应用前景。
Background and Objectives: In clinical trials involving multiple tests it is often difficult to obtain informative simultaneous confidence intervals (SCIs). In particular in hierarchical testing, no quantification of effects is possible for the first tested (and most important) hypothesis after its rejection. Our goal is a construction of SCIs that are always informative.Methods: We present an approach where the level is split after rejection of each hypothesis to obtain an informative confidence bound. The splitting weights are continuous functions of the parameters. Our method is realizable by a simple algorithm and is illustrated by an intuitive graphical representation.Results: We show theoretically and by an example that the new Sas always provide information when a hypothesis is rejected. The power to reject the first hypothesis is not smaller than for the classical fixed-sequence procedure. The price for the extra information is a small power loss in the hypotheses proceeding the most important one.Conclusions: Given the substantial gain in information, a small loss of power for the non-primary hypotheses seems often acceptable. Especially in the context of non-inferiority trials, this method is a useful alternative. The flexibility in the choice of the weight ' functions makes the procedure attractive for applications.