近期分子靶向肿瘤药物开发取得的实质性进展，要求我们采用新的早期临床试验方法学范式，使我们能够同时和高效地评估多个亚型的疗效。篮子试验的概念引起了广泛关注，以实现跨亚型借鉴信息的要求，这些亚型被称为篮子。贝叶斯方法是实现这一目标的自然途径，事实上，现有提案中的大多数都依赖于此方法。然而，贝叶斯方法需要复杂的建模，并且不一定能以名义水平控制第一类错误概率。在本文中，我们基于一样本Mantel-Haenszel程序提出了一种完全频率主义的篮子试验方法，依赖于在篮子间共同治疗效应假设下借鉴信息的非常简单的思想。我们证明了所提出的治疗效应的Mantel-Haenszel估计在大横纵比和稀疏数据极限模型下是一致的，并提出了一致的方差估计器。即使共同治疗效应假设被违背，所提出的估计器也是可解释的。然后，我们可以以确认的方式设计篮子试验。我们还提出了一种信息准则方法来识别有效的篮子子类。© 2023 John Wiley & Sons, Ltd.

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 article, 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 Mantel-Haenszel estimator for the treatment effect 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 estimators are interpretable even if the common treatment effect assumptions are violated. Then, we can design basket trials in a confirmatory matter. We also propose an information criterion approach to identify effective subclasses of baskets.© 2023 John Wiley & Sons, Ltd.