Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.
Department of Mathematics and Statistics, Lancaster University, Lancashire, UK.
Stat Methods Med Res. 2021 Apr;30(4):1057-1071. doi: 10.1177/0962280220986580. Epub 2021 Jan 27.
In this paper, we develop a general Bayesian hierarchical model for bridging across patient subgroups in phase I oncology trials, for which preliminary information about the dose-toxicity relationship can be drawn from animal studies. Parameters that re-scale the doses to adjust for intrinsic differences in toxicity, either between animals and humans or between human subgroups, are introduced to each dose-toxicity model. Appropriate priors are specified for these scaling parameters, which capture the magnitude of uncertainty surrounding the animal-to-human translation and bridging assumption. After mapping data onto a common, 'average' human dosing scale, human dose-toxicity parameters are assumed to be exchangeable either with the standardised, animal study-specific parameters, or between themselves across human subgroups. Random-effects distributions are distinguished by different covariance matrices that reflect the between-study heterogeneity in animals and humans. Possibility of non-exchangeability is allowed to avoid inferences for extreme subgroups being overly influenced by their complementary data. We illustrate the proposed approach with hypothetical examples, and use simulation to compare the operating characteristics of trials analysed using our Bayesian model with several alternatives. Numerical results show that the proposed approach yields robust inferences, even when data from multiple sources are inconsistent and/or the bridging assumptions are incorrect.
在本文中，我们开发了一种通用的贝叶斯分层模型，用于在肿瘤学 I 期临床试验中跨越患者亚组，对于这些临床试验，可以从动物研究中获得关于剂量-毒性关系的初步信息。我们为每个剂量-毒性模型引入了参数，这些参数可以重新调整剂量，以调整毒性在动物和人类之间或人类亚组之间的内在差异。为这些缩放参数指定了适当的先验，这些参数捕捉了围绕动物到人类转化和桥接假设的不确定性的大小。在将数据映射到共同的“平均”人类剂量尺度之后，假设人类剂量-毒性参数可以与标准化的、特定于动物研究的参数交换，或者在人类亚组之间相互交换。随机效应分布通过不同的协方差矩阵来区分，这些协方差矩阵反映了动物和人类之间的研究间异质性。允许非可交换性的存在，以避免对极端亚组的推断受到其互补数据的过度影响。我们通过假设示例来说明所提出的方法，并使用模拟来比较使用我们的贝叶斯模型分析的试验与几种替代方法的操作特征。数值结果表明，即使来自多个来源的数据不一致和/或桥接假设不正确，所提出的方法也能得出稳健的推断。
