在癌症研究中，篮子试验旨在评估使用篮子的药物的功效，其中根据患者的肿瘤类型将患者分为亚组。在这种情况下，使用信息借用策略可以通过将表征具有相似药物活性的篮子中的药物功效的参数的估计缩小到一起来增加检测活性篮子中药物功效的概率。在这里，我们建议使用融合惩罚逻辑回归模型在具有二元结果的第二阶段单臂篮子试验的设置中借用信息。我们描述了我们提出的策略并通过模拟研究评估其性能。我们评估了药物疗效的异质性、每种肿瘤类型的患病率以及对我们提出的设计的操作特征进行中期分析的影响。我们将我们的方法与两种现有的设计进行了比较，依靠贝叶斯框架中先验信息的规范来借用类似篮子的信息。值得注意的是，当药物的效果在不同的篮子中差异很大时，我们的方法表现良好。我们的方法具有多种优势，包括有限的实施工作和快速计算，这在规划新试验时至关重要，因为此类规划需要深入的模拟研究。

In cancer research, basket trials aim to assess the efficacy of a drug using baskets, wherein patients are organized into subgroups according to their tumor type. In this context, using information borrowing strategy may increase the probability of detecting drug efficacy in active baskets, by shrinking together the estimates of the parameters characterizing the drug efficacy in baskets with similar drug activity. Here, we propose to use fusion-penalized logistic regression models to borrow information in the setting of a phase 2 single-arm basket trial with binary outcome. We describe our proposed strategy and assess its performance via a simulation study. We assessed the impact of heterogeneity in drug efficacy, prevalence of each tumor types and implementation of interim analyses on the operating characteristics of our proposed design. We compared our approach with two existing designs, relying on the specification of prior information in a Bayesian framework to borrow information across similar baskets. Notably, our approach performed well when the effect of the drug varied greatly across the baskets. Our approach offers several advantages, including limited implementation efforts and fast computation, which is essential when planning a new trial as such planning requires intensive simulation studies.