Regulatory authorities guide the use of permutation tests or randomization tests so as not to increase the type-I error rate when applying covariate-adaptive randomization in randomized clinical trials. For non-inferiority and equivalence trials, this paper derives adjusted confidence intervals using permutation and randomization methods, thus controlling the type-I error to be much closer to the pre-specified nominal significance level. We consider three variable types for the outcome of interest, namely normal, binary, and time-to-event variables for the adjusted confidence intervals. For normal variables, we show that the type-I error for the adjusted confidence interval holds the nominal significance level. However, we highlight a unique theoretical challenge for non-inferiority and equivalence trials: binary and time-to-event variables may not hold the nominal significance level when the model parameters are estimated by models that diverge from the data-generating model under the null hypothesis. To clarify these features, we present simulation results and evaluate the performance of the adjusted confidence intervals.

翻译：监管机构指导使用置换检验或随机化检验，以在随机对照试验中应用协变量自适应随机化时避免增加I类错误率。针对非劣效性和等效性试验，本文基于置换方法和随机化方法推导了调整后的置信区间，从而将I类错误率控制在更接近预设名义显著性水平的水平。我们考虑了三种结局变量类型来构建调整后的置信区间，即正态变量、二分类变量和生存时间变量。对于正态变量，我们证明调整后的置信区间对应的I类错误率能达到名义显著性水平。然而，我们强调非劣效性与等效性试验中存在的独特理论挑战：当采用与零假设下数据生成模型不同的模型来估计参数时，二分类变量和生存时间变量可能无法达到名义显著性水平。为阐明这些特性，我们展示了模拟结果并评估了调整后置信区间的性能。