Factorial design considerations
Factorial design considerations
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DOI：
10.1200/jco.2002.03.003
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发表时间：
2002-08-15
影响因子：
45.3
通讯作者：
O'Sullivan, J
中科院分区：
文献类型：
--
作者：
Green, S;Liu, PY;O'Sullivan, J
目的：析因设计可用于检验临床试验中的额外问题。析因试验的样本量和分析的常见方法假设没有统计学相互作用，并且不对多重检验进行调整。这项调查考虑了用更少的患者测试更多问题的潜在收益与析因试验得出错误结论的频率之间的权衡。方法：2 × 2设计的仿真研究（观察V化疗V放疗V联合）在各种条件下进行，包括一种，两种，或既不治疗，也不存在或不存在统计学相互作用（一种治疗的效果根据另一种治疗的存在而不同）。研究了三种分析方法，一种假设没有相互作用，第二种测试首先是相互作用，第三种测试是相互作用以及调整多重测试。方法进行了比较，就选择正确的治疗arm.Results的概率：没有一个方法是上级。互动测试在某些情况下是有益的，但在其他情况下是有害的。在某些情况下，析因设计提高了效率，但在其他情况下，所有三种方法导致在选择正确的治疗臂在trial.Conclusion结束时的概率差：额外的效率是可能的，但它是很难预测的有利条件存在。如果使用析因设计，则应权衡潜在的效率增益与潜在的功效损失，以在可能的相关情景下得出正确的结论。(C)2002年，美国临床肿瘤学会。
Purpose: Factorial designs may be proposed to test extra questions within a clinical trial. A common approach to sample size and analysis for factorial trials assumes no statistical interactions and does not adjust for multiple testing. This investigation considered the trade-off between potential gains from testing more questions with fewer patients versus how often a factorial trial might arrive at an incorrect conclusion.Methods: A simulation study of a 2 x 2 design (observation v chemotherapy v radiation therapy v the combination) was performed under various conditions, including effect of one, both, or neither treatment and absence or presence of statistical interaction (effect of one treatment differed according to the presence of the other). Three analysis approaches were investigated, one assuming no interaction, a second testing first for interaction, and the third testing for interaction as well as adjusting for multiple testing. The approaches were compared with respect to the probability of selecting the correct treatment arm.Results: No one approach was superior. Testing for interaction was beneficial in some settings but detrimental in others. Under some scenarios, the factorial design improved efficiency, but under others, all three approaches resulted in poor probability of selecting the correct treatment arm at the end of the trial.Conclusion: Extra efficiency is possible, but it is difficult to predict when favorable conditions exist. If a factorial design is used, potential efficiency gains should be weighed against potential loss of power to arrive at the correct conclusion under possible scenarios of interest. (C) 2002 by American Society of Clinical Oncology.