Department of Biostatistics, Indiana University, Indianapolis, Indiana.
Center for Computational Biology and Bioinformatics, Indiana University, Indianapolis, Indiana.
Stat Med. 2019 Jul 10;38(15):2883-2896. doi: 10.1002/sim.8159. Epub 2019 Apr 9.
It is well known that the treatment effect of a molecularly targeted agent (MTA) may vary dramatically, depending on each patient's biomarker profile. Therefore, for a clinical trial evaluating MTA, it is more reasonable to evaluate its treatment effect within different marker subgroups rather than evaluating the average treatment effect for the overall population. The marker-stratified design (MSD) provides a useful tool to evaluate the subgroup treatment effects of MTAs. Under the Bayesian framework, the beta-binomial model is conventionally used under the MSD to estimate the response rate and test the hypothesis. However, this conventional model ignores the fact that the biomarker used in the MSD is, in general, predictive only for the MTA. The response rates for the standard treatment can be approximately consistent across different subgroups stratified by the biomarker. In this paper, we proposed a Bayesian hierarchical model incorporating this biomarker information into consideration. The proposed model uses a hierarchical prior to borrow strength across different subgroups of patients receiving the standard treatment and, therefore, improve the efficiency of the design. Prior informativeness is determined by solving a "customized" equation reflecting the physician's professional opinion. We developed a Bayesian adaptive design based on the proposed hierarchical model to guide the treatment allocation and test the subgroup treatment effect as well as the predictive marker effect. Simulation studies and a real trial application demonstrate that the proposed design yields desirable operating characteristics and outperforms the existing designs.
众所周知，分子靶向药物（MTA）的治疗效果可能因每位患者的生物标志物特征而有很大差异。因此，对于评估 MTA 的临床试验，在不同的标志物亚组内评估其治疗效果比评估总体人群的平均治疗效果更为合理。标志物分层设计（MSD）为评估 MTA 的亚组治疗效果提供了有用的工具。在贝叶斯框架下，传统上在 MSD 下使用β-二项式模型来估计反应率并检验假设。然而，这种传统模型忽略了一个事实，即在 MSD 中使用的生物标志物通常仅对 MTA 具有预测性。根据生物标志物分层的标准治疗的反应率在不同亚组中大致保持一致。在本文中，我们提出了一种贝叶斯层次模型，将这种生物标志物信息纳入考虑。所提出的模型使用层次先验来跨接受标准治疗的不同患者亚组借用强度，从而提高设计效率。先验信息性是通过求解反映医生专业意见的“定制”方程来确定的。我们基于所提出的层次模型开发了一种贝叶斯自适应设计，以指导治疗分配和检验亚组治疗效果以及预测标志物效果。模拟研究和实际试验应用表明，所提出的设计具有理想的操作特性，并优于现有设计。
