Treatment-covariate interaction tests are commonly applied by researchers to examine whether the treatment effect varies across patient subgroups defined by baseline characteristics. The objective of this study is to explore treatment-covariate interaction tests involving covariate-adaptive randomization. Without assuming a parametric data generation model, we investigate usual interaction tests and observe that they tend to be conservative: specifically, their limiting rejection probabilities under the null hypothesis do not exceed the nominal level and are typically strictly lower than it. To address this problem, we propose modifications to the usual tests to obtain corresponding exact tests. Moreover, we introduce a novel class of stratified-adjusted interaction tests that are simple, broadly applicable, and more powerful than the usual and modified tests. Our findings are relevant to two types of interaction tests: one involving stratification covariates and the other involving additional covariates that are not used for randomization.

翻译：研究者常采用处理-协变量交互作用检验，以探究治疗效果是否因基线特征定义的患者亚组而异。本研究旨在探讨涉及协变量自适应随机化的处理-协变量交互作用检验。在不假设参数化数据生成模型的前提下，我们考察了常规交互作用检验，发现其趋于保守：具体而言，原假设下检验的极限拒绝概率不超过名义水平，且通常严格低于该水平。针对此问题，我们提出对常规检验的修改方案，以构建相应的精确检验。此外，我们引入了一类新颖的分层调整交互作用检验，该方法简洁、适用广泛，且相较于常规检验与修改检验具有更高的检验效能。本研究的结果适用于两类交互作用检验：一类涉及分层协变量，另一类涉及未用于随机化的额外协变量。