Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.
MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Pharm Stat. 2021 Nov;20(6):990-1001. doi: 10.1002/pst.2119. Epub 2021 Mar 24.
Umbrella trials are an innovative trial design where different treatments are matched with subtypes of a disease, with the matching typically based on a set of biomarkers. Consequently, when patients can be positive for more than one biomarker, they may be eligible for multiple treatment arms. In practice, different approaches could be applied to allocate patients who are positive for multiple biomarkers to treatments. However, to date there has been little exploration of how these approaches compare statistically. We conduct a simulation study to compare five approaches to handling treatment allocation in the presence of multiple biomarkers - equal randomisation; randomisation with fixed probability of allocation to control; Bayesian adaptive randomisation (BAR); constrained randomisation; and hierarchy of biomarkers. We evaluate these approaches under different scenarios in the context of a hypothetical phase II biomarker-guided umbrella trial. We define the pairings representing the pre-trial expectations on efficacy as linked pairs, and the other biomarker-treatment pairings as unlinked. The hierarchy and BAR approaches have the highest power to detect a treatment-biomarker linked interaction. However, the hierarchy procedure performs poorly if the pre-specified treatment-biomarker pairings are incorrect. The BAR method allocates a higher proportion of patients who are positive for multiple biomarkers to promising treatments when an unlinked interaction is present. In most scenarios, the constrained randomisation approach best balances allocation to all treatment arms. Pre-specification of an approach to deal with treatment allocation in the presence of multiple biomarkers is important, especially when overlapping subgroups are likely.
伞式试验是一种创新的试验设计，其中不同的治疗方法与疾病的亚型相匹配，匹配通常基于一组生物标志物。因此，当患者可以对多个生物标志物呈阳性时，他们可能有资格接受多个治疗组。在实践中，可以应用不同的方法将对多个生物标志物呈阳性的患者分配到治疗中。然而，迄今为止，对于这些方法如何在统计学上进行比较，研究甚少。我们进行了一项模拟研究，以比较在存在多个生物标志物的情况下处理治疗分配的五种方法——均等随机化；随机化与固定控制分配概率；贝叶斯自适应随机化（BAR）；约束随机化；以及生物标志物分层。我们在一个假设的基于生物标志物的 II 期伞式试验的背景下，在不同情况下评估这些方法。我们将代表预先对疗效的期望的配对定义为关联配对，而其他生物标志物-治疗配对定义为非关联配对。层次和 BAR 方法具有最高的检测与治疗-生物标志物相关的相互作用的功效。然而，如果预先指定的治疗-生物标志物配对不正确，层次程序的性能就会很差。当存在非关联相互作用时，BAR 方法会将更多对多个生物标志物呈阳性的患者分配到有希望的治疗方法中。在大多数情况下，约束随机化方法最好地平衡了所有治疗组的分配。在存在多个生物标志物的情况下处理治疗分配的方法的预先指定很重要，特别是当重叠亚组很可能存在时。
