Research in oncology has changed the focus from histological properties of tumors in a specific organ to a specific genomic aberration potentially shared by multiple cancer types. This motivates the basket trial, which assesses the efficacy of treatment simultaneously on multiple cancer types that have a common aberration. Although the assumption of homogeneous treatment effects seems reasonable given the shared aberration, in reality, the treatment effect may vary by cancer type, and potentially only a subgroup of the cancer types respond to the treatment. Various approaches have been proposed to increase the trial power by borrowing information across cancer types, which, however, tend to inflate the type I error rate. In this article, we review some representative Bayesian information borrowing methods for the analysis of early‐phase basket trials. We then propose a novel method called the Bayesian hierarchical model with a correlated prior (CBHM), which conducts more flexible borrowing across cancer types according to sample similarity. We did simulation studies to compare CBHM with independent analysis and three information borrowing approaches: the conventional Bayesian hierarchical model, the EXNEX approach, and Liu's two‐stage approach. Simulation results show that all information borrowing approaches substantially improve the power of independent analysis if a large proportion of the cancer types truly respond to the treatment. Our proposed CBHM approach shows an advantage over the existing information borrowing approaches, with a power similar to that of EXNEX or Liu's approach, but the potential to provide substantially better control of type I error rate.

中文翻译：

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贝叶斯方法用于早期癌症篮子试验的分析，并借用了跨癌症类型的信息。

肿瘤学研究已将重点从特定器官中肿瘤的组织学特性改变为多种癌症类型可能共享的特定基因组畸变。这激发了篮子试验，该试验同时评估了具有常见畸变的多种癌症类型的治疗效果。尽管考虑到共享像差，假设均质治疗效果是合理的，但实际上，治疗效果可能因癌症类型而异，并且可能只有一部分癌症类型对治疗产生反应。已经提出了各种方法来通过借用癌症类型之间的信息来增加试验能力，但是，这往往会增加I型错误率。在这篇文章中，我们回顾了一些有代表性的贝叶斯信息借用方法，用于早期篮子试验的分析。然后，我们提出了一种新的方法，称为具有相关先验（CBHM）的贝叶斯层次模型，该方法根据样本相似性在癌症类型之间进行更灵活的借用。我们进行了仿真研究，以将CBHM与独立分析和三种信息借用方法进行比较：常规贝叶斯层次模型，EXNEX方法和Liu的两阶段方法。模拟结果表明，如果很大一部分癌症类型真正对治疗产生反应，则所有信息借用方法都会大大提高独立分析的能力。我们提出的CBHM方法显示出优于现有信息借用方法的优势，