AI 中文总结
针对协变量自适应随机化下广义线性模型多臂试验，提出修正Wald统计量分布问题的调整检验统计量，结合Simes型多重检验程序控制一类错误，经模拟和真实试验验证有效。
AI 中文摘要
现代医学研究（如剂量探索研究、无缝试验和共享对照设计）常涉及同时比较多种治疗方案。尽管其应用广泛，但多数研究聚焦于连续型终点，对一般结局类型的推断方法存在大量需求。本文针对协变量自适应随机化（CAR）下广义线性模型（GLM）框架内涉及多种治疗比较的终点，提出一种新的推断方法。首先，研究多臂试验中标准Wald z统计量（z分数）的渐近性质，强调当工作模型误设（尤其是遗漏协变量时）出现的问题。理论结果表明，这些z分数不会一致收敛到标准多元正态分布，根据具体GLM终点的不同，会导致一类错误率偏保守或膨胀。其次，基于这些理论结果，开发调整后的检验统计量以修正分布问题。为适当控制多臂比较中固有的家族式一类错误率，将调整后的统计量与Simes型多重检验程序结合。该稳健推断方法可有效控制一类错误，同时可能提升检验效能。大量模拟研究及对转移性乳腺癌试验的实际应用，证实了本文方法的有效性和实用性。
英文摘要
Modern medical research, such as dose-finding studies, seamless trials, and shared control designs, often involves comparing multiple treatments simultaneously. Despite its wide applications, most research focuses on continuous endpoints, leaving the inference for general outcome types in high demand. In this article, we propose a new inference method for conducting multiple-treatment comparisons involving endpoints within the generalized linear model (GLM) framework under covariate-adaptive randomization (CAR). First, we investigate the asymptotic properties of the standard Wald z-statistics (z-scores) in multi-arm trials, highlighting issues when the working model is misspecified, particularly through omitted covariates. Our theoretical findings reveal that these \textcolor{black}{z-scores} do not consistently converge to a standard multivariate normal distribution, leading to either conservative or inflated Type I error rates, depending on the specific GLM endpoint. Second, based on these theoretical results, we develop adjusted test statistics to correct the distributional problems. To appropriately control the family-wise Type I error rate inherent in multi-arm comparisons, we incorporate our adjusted statistics with Simes-type multiple-testing procedures. This robust inference method can effectively control Type I error while potentially improving power. Extensive simulation studies and a real-world application to a metastatic breast cancer trial confirm the effectiveness and practicality of our approach.
Comments38 pages
