Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Rockville, MD.
National Institutes of Health, National Institute of Allergy and Infectious Diseases, Rockville, MD.
Stat Med. 2019 May 30;38(12):2292-2302. doi: 10.1002/sim.8093. Epub 2019 Jan 22.
As randomization methods use more information in more complex ways to assign patients to treatments, analysis of the resulting data becomes challenging. The treatment assignment vector and outcome vector become correlated whenever randomization probabilities depend on data correlated with outcomes. One straightforward analysis method is a re-randomization test that fixes outcome data and creates a reference distribution for the test statistic by repeatedly re-randomizing according to the same randomization method used in the trial. This article reviews re-randomization tests, especially in nonstandard settings like covariate-adaptive and response-adaptive randomization. We show that re-randomization tests provide valid inference in a wide range of settings. Nonetheless, there are simple examples demonstrating limitations.
由于随机化方法以更复杂的方式使用更多信息来分配患者接受治疗，因此分析由此产生的数据变得具有挑战性。只要随机化概率取决于与结果相关的数据，那么治疗分配向量和结果向量就会相关。一种简单的分析方法是重新随机化检验，该检验固定结果数据，并通过根据与试验中使用的相同随机化方法重复重新随机化，为检验统计量创建参考分布。本文回顾了重新随机化检验，特别是在协变量自适应和反应自适应随机化等非标准设置下。我们表明，重新随机化检验在广泛的设置中提供了有效的推断。尽管如此，仍有简单的示例表明了其局限性。
