For the analysis of clinical trials, the study participants are usually assumed to be representative sample of a target population. This assumption is rarely fulfilled in clinical trials, and particularly not if the sample size is small. In addition, covariate imbalances may affect the trial. Randomization tests provide a nonparametric analysis method of the treatment effect that does not rely on population‐based assumptions. We propose a nonparametric statistical model that yields a formal basis for randomization tests. We adapt the model for the presence of covariate imbalance in the form of selection bias and investigate the effects of bias on the rejection probability of the randomization test using Monte Carlo simulations. Finally, we show that ancillary statistics can be used to control for the influence of bias. We show that covariate imbalance leads to an inflation of the type I error probability. The proposed nonparametric model allows for the use of ancillary statistics that yield an unbiased adjusted randomization test.

中文翻译：

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存在选择偏差时基于随机化的推断

为了进行临床试验分析，通常假定研究参与者是目标人群的代表性样本。这种假设在临床试验中很少能实现，尤其是在样本量较小的情况下。此外，协变量失衡可能会影响试验。随机检验提供了一种不依赖人群假设的治疗效果的非参数分析方法。我们提出了一种非参数统计模型，该模型为随机检验提供了正式基础。我们以选择偏倚的形式针对协变量不平衡的存在调整模型，并使用蒙特卡洛模拟研究偏倚对随机检验拒绝概率的影响。最后，我们表明可以使用辅助统计数据来控制偏见的影响。我们证明协变量不平衡导致I型错误概率的膨胀。拟议的非参数模型允许使用产生无偏调整随机检验的辅助统计数据。