A randomization-based perspective on analysis of variance: a test statistic robust to treatment effect heterogeneity
A randomization-based perspective on analysis of variance: a test statistic robust to treatment effect heterogeneity
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基于随机化的方差分析视角：对治疗效果异质性稳健的检验统计量
DOI：
10.1093/biomet/asx059
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发表时间：
2017
期刊：
影响因子：
2.7
通讯作者：
Dasgupta, Tirthankar
中科院分区：
文献类型：
--
作者：
Ding, Peng;Dasgupta, Tirthankar
在与涉及两种以上治疗的完全随机化实验相关的有限人群环境中，考虑了Neyman无平均治疗效应的零假设的Fisher随机化检验。使用统计量进行这样一个测试的后果进行检查，我们认为，治疗效果的异质性，使用统计量在Fisher随机化检验可以严重膨胀下奈曼的零假设的I型错误。我们建议使用一个替代的检验统计量，推导出其渐近分布下的Fisher和奈曼的零假设，并通过模拟证明其优势。
SummaryFisher randomization tests for Neyman’s null hypothesis of no average treatment effect are considered in a finite-population setting associated with completely randomized experiments involving more than two treatments. The consequences of using thestatistic to conduct such a test are examined, and we argue that under treatment effect heterogeneity, use of thestatistic in the Fisher randomization test can severely inflate the Type I error under Neyman’s null hypothesis. We propose to use an alternative test statistic, derive its asymptotic distributions under Fisher’s and Neyman’s null hypotheses, and demonstrate its advantages through simulations.