Stepwise Confidence Intervals without Multiplicity Adjustment for Dose—Response and Toxicity Studies
Stepwise Confidence Intervals without Multiplicity Adjustment for Dose—Response and Toxicity Studies
复制标题
剂量反应和毒性研究中无需多重调整的逐步置信区间
DOI：
10.1080/01621459.1999.10474141
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发表时间：
1999
影响因子：
3.7
通讯作者：
R. Berger
中科院分区：
文献类型：
--
作者：
J. Hsu;R. Berger
摘要并非所有的同时推理都需要多重性调整。如果单个推理的顺序是预先定义的，并且在任何步骤中未能实现期望的推理使得后续推理变得不必要，则不需要多重性调整。这可以使用封闭测试原理来证明，以测试顺序嵌套的适当假设，从最严格的假设开始。但在某些问题中，什么样的假设是合适的可能并不明显。我们给出了一个根本不同的，基于置信集的理由，自然分区的参数空间，并使用的原则，正是一个成员的分区包含真正的参数。在剂量反应研究中，旨在显示治疗优于安慰剂（阴性对照）或已知有效的药物（活性对照），置信集方法生成具有有意义的保证的方法，以防止错误的决定，而以前的应用封闭测试应用程序...
Abstract Not all simultaneous inferences need multiplicity adjustment. If the sequence of individual inferences is predefined, and failure to achieve the desired inference at any step renders subsequent inferences unnecessary, then multiplicity adjustment is not needed. This can be justified using the closed testing principle to test appropriate hypotheses that are nested in sequence, starting with the most restrictive one. But what hypotheses are appropriate may not be obvious in some problems. We give a fundamentally different, confidence set–based justification by partitioning the parameter space naturally and using the principle that exactly one member of the partition contains the true parameter. In dose–response studies designed to show superiority of treatments over a placebo (negative control) or a drug known to be efficacious (active control), the confidence set approach generates methods with meaningful guarantee against incorrect decision, whereas previous applications of the closed testing app...