Randomization inference and bias of standard errors
Randomization inference and bias of standard errors
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DOI：
10.1198/000313001753272268
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发表时间：
2001-11-01
影响因子：
1.8
通讯作者：
Gadbury, GL
中科院分区：
文献类型：
--
作者：
Gadbury, GL
一门非参数统计课程通常包括关于基于“无分布”随机化推理的材料。然而，报告的p值和可信区间的准确性通常依赖于对单位(受试者)治疗相加性的不可验证的假设。这一假设并不总是在文本中明确说明，当假设不成立时，对推理的影响很少讨论。本文的重点是在存在非加性的情况下，估计平均处理效应的标准误差的偏差。在三种常见的实验设计中，这种偏差被描述和解释为标准误差的通常估计：两样本完全随机设计、配对设计和平衡的两周期交叉设计。即使在非加性存在的情况下，也可以得到对平均治疗效果的有用的保守估计。这一点使用了一些以前发布的数据进行了说明。
A nonparametric statistics course typically includes material regarding "distribution free" randomization based inference. However, the accuracy of reported p values and confidence intervals often relies on an unverifiable assumption of unit(subject)treatment additivity. This assumption is not always explicitly stated in texts and, when the assumption does not hold, the implications on inference are seldom discussed. The focus of this article is the bias of standard errors of estimated mean treatment effects in the presence of nonadditivity. This bias is characterized and interpreted for a usual estimator of standard, error in three common experimental designs: a two-sample completely randomized design, a matched-pairs design, and a balanced, two-period cross-over design. Even in the presence of nonadditivity, useful conservative estimates of a mean treatment effect can be obtained. This is illustrated using some previously published data.