Recent substantial advances of molecular targeted oncology drug development is requiring new paradigms for early-phase clinical trial methodologies to enable us to evaluate efficacy of several subtypes simultaneously and efficiently. The concept of the basket trial is getting of much attention to realize this requirement borrowing information across subtypes, which are called baskets. Bayesian approach is a natural approach to this end and indeed the majority of the existing proposals relies on it. On the other hand, it required complicated modeling and may not necessarily control the type 1 error probabilities at the nominal level. In this paper, we develop a purely frequentist approach for basket trials based on one-sample Mantel-Haenszel procedure relying on a very simple idea for borrowing information under the common treatment effect assumption over baskets. We show that the proposed estimator is consistent under two limiting models of the large strata and sparse data limiting models (dually consistent) and propose dually consistent variance estimators. The proposed Mantel-Haenszel estimators are interpretable even if the common treatment assumptions are violated. Then, we can design basket trials in a confirmatory matter. We also propose an information criterion approach to identify effective subclass of baskets.

翻译：近期分子靶向肿瘤药物开发的重大进展需要新的早期临床试验方法学范式，以便能够高效地同时评估多个亚型的疗效。篮子试验的概念正受到广泛关注，该方法通过跨亚型（即所谓的"篮子"）借用信息来实现这一需求。贝叶斯方法自然适用于此目标，事实上现有的大多数提案都基于此原理。然而，这类方法需要复杂的建模，且未必能将第一类错误概率控制在名义水平。本文基于单样本Mantel-Haenszel方法，开发了纯粹的频率学派篮子试验分析方法，该方法依托于在跨篮子共同处理效应假设下借用信息的极简理念。我们证明所提出的估计量在两种极限模型（大分层模型和稀疏数据极限模型）下具有一致性（双重一致性），并提出了双重一致性方差估计量。即使共同处理假设被违反，所提出的Mantel-Haenszel估计量仍具有可解释性。据此，我们能够以验证性方式设计篮子试验。此外，我们还提出了一种信息准则方法，用于识别有效的篮子亚群。