Flexible evaluation of surrogacy in Bayesian adaptive platform studies
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贝叶斯自适应平台研究中替代终点的灵活评价
- 研究问题
- 在研究人群、治疗和实施方式均可能异质的贝叶斯自适应平台试验中，如何评价替代终点并识别其有效性不同的群组？
- 方法与思路
- 作者提出分层贝叶斯半参数模型，以 Dirichlet 过程混合刻画真实效应分布，灵活建模替代终点与主要结局的治疗效应关系。
- 材料陈述的贡献
- 摘要称，模拟中该方法优于一个常见分层贝叶斯基准，并在基于 ProBio 的模拟示例中识别替代终点有用与无用的群组。
- 阅读关注点
- 应核对替代终点有效性的定义、效应估计误差的处理、自适应设计的纳入方式，以及聚类和基准比较的评价标准。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；ProBio 应用仍是模拟示例，真实试验应用尚属计划，摘要未给数值结果。
原文摘要
Trial level surrogates are useful tools for improving the speed and cost effectiveness of trials, but surrogates that have not been properly evaluated can cause misleading results. The evaluation procedure is often contextual and depends on the type of trial setting. There have been many proposed methods for trial level surrogate evaluation, but none, to our knowledge, for the specific setting of Bayesian adaptive platform studies. As adaptive studies are becoming more popular, methods for surrogate evaluation using them are needed. These studies also offer a rich data resource for surrogate evaluation that would not normally be possible. However, they also offer a set of statistical issues including heterogeneity of the study population, treatments, implementation, and even potentially the quality of the surrogate. We propose the use of a hierarchical Bayesian semiparametric model for the evaluation of potential surrogates using nonparametric priors for the distribution of true effects based on Dirichlet process mixtures. The motivation for this approach is to flexibly model relationships between the treatment effect on the surrogate and the treatment effect on the outcome and also to identify potential clusters with differential surrogate value in a data-driven manner. In simulations, we find that our proposed method is superior to a simple, but fairly standard, hierarchical Bayesian method. We demonstrate how our method can be used in a simulated illustrative example (based on the ProBio trial), in which we are able to identify clusters where the surrogate is, and is not useful. We plan to apply our method to the ProBio trial, once it is completed.