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 generating 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 valid tests. Moreover, we introduce a novel class of stratified-adjusted interaction tests that are simple, more powerful than the usual and modified tests, and broadly applicable to most covariate-adaptive randomization methods. The results are general to encompass two types of interaction tests: one involving stratification covariates and the other involving additional covariates that are not used for randomization. Our study clarifies the application of interaction tests in clinical trials and offers valuable tools for revealing treatment heterogeneity, crucial for advancing personalized medicine.

翻译：研究人员常采用处理-协变量交互检验，以考察治疗效果是否因基线特征定义的患者亚组而异。本研究旨在探索涉及协变量自适应随机化的处理-协变量交互检验。在不假设参数化数据生成模型的前提下，我们研究了常规交互检验，发现其往往趋于保守：具体而言，在零假设下其极限拒绝概率不超过名义水平，且通常严格低于该水平。针对这一问题，我们提出了对常规检验的修正方案，以获得相应的有效检验。此外，我们引入了一类全新的分层调整交互检验，该方法简洁、效力优于常规及修正检验，且广泛适用于大多数协变量自适应随机化方法。研究结果具有普适性，涵盖两类交互检验：一类涉及分层协变量，另一类涉及未用于随机化的附加协变量。本研究阐明了交互检验在临床试验中的应用，并为揭示治疗异质性提供了有价值的工具，对推动个性化医疗具有重要意义。