Ordered multiple comparisons with the best and their applications to dose-response studies
Ordered multiple comparisons with the best and their applications to dose-response studies
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DOI：
10.1111/j.1541-0420.2007.00813.x
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发表时间：
2007-12-01
期刊：
影响因子：
1.9
通讯作者：
Finner, H.
中科院分区：
文献类型：
--
作者：
Strassburger, K.;Bretz, F.;Finner, H.
本文考虑比较几种治疗方法(剂量水平、干预措施等)的问题。最好的，最好的治疗是未知的，治疗在某种意义上是有序的。在实践中，治疗之间的顺序关系经常非常自然地发生。例如，可以根据不断增加的风险来订购它们，例如，在剂量-反应研究中，随着剂量水平的增加，耐受性或安全问题。我们解决的问题是，为所有治疗中最小的指数最多比具有最大效果的(最好的)治疗效果略低的治疗构建一个较低的置信限。这样的界限确保在置信度为1-α的情况下，所有指数较低的治疗方法的有效性相对低于最好的竞争对手。我们导出了一种多重测试策略，该策略导致了明显的置信度。将所提出的置信度下限与其他测试策略得到的置信度下限进行了比较。我们进一步推导出功率和样本量计算的封闭形式的表达式。最后，我们调查了几个真实的数据集，以说明我们方法的各种应用。
This article considers the problem of comparing several treatments (dose levels, interventions, etc.) with the best, where the best treatment is unknown and the treatments are ordered in some sense. Order relations among treatments often occur quite naturally in practice. They may be ordered according to increasing risks, such as tolerability or safety problems with increasing dose levels in a dose-response study, for example. We tackle the problem of constructing a lower confidence bound for the smallest index of all treatments being at most marginally less effective than the (best) treatment having the largest effect. Such a bound ensures at confidence level 1 - alpha that all treatments with lower indices are relevantly less effective than the best competitor. We derive a multiple testing strategy that results in sharp confidence bounds. The proposed lower confidence bound is compared with those derived from other testing strategies. We further derive closed-form expressions for power and sample size calculations. Finally, we investigate several real data sets to illustrate various applications of our methods.