Department of Public Health, Section of Biostatistics, University of Copenhagen, Øster Farimagsgade 5, 1353 København K, Denmark.
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, Stockholm 17177, Sweden.
Biostatistics. 2023 Dec 15;25(1):220-236. doi: 10.1093/biostatistics/kxac053.
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 platform studies. As platform 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 so that treatment effects on the surrogate can be used to reliably predict treatment effects on the clinical outcome. 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.
试验水平替代指标是提高试验速度和成本效益的有用工具，但未经适当评估的替代指标可能会导致误导性结果。评估程序通常是上下文相关的，取决于试验设置的类型。已经提出了许多用于试验水平替代指标评估的方法，但据我们所知，没有一种方法适用于平台研究的特定设置。随着平台研究越来越受欢迎，需要使用它们的替代指标评估方法。这些研究还提供了一个丰富的数据资源，用于通常不可能进行的替代指标评估。然而，它们还提供了一组统计问题，包括研究人群、治疗方法、实施情况，甚至潜在的替代指标的质量的异质性。我们提出使用分层贝叶斯半参数模型来评估潜在的替代指标，该模型使用基于狄利克雷过程混合的非参数先验来估计真实效果的分布。这种方法的动机是灵活地建模替代指标上的治疗效果与结局上的治疗效果之间的关系，并且以数据驱动的方式识别潜在的具有不同替代价值的聚类，以便能够可靠地预测替代指标上的治疗效果。在模拟中，我们发现我们提出的方法优于一种简单但相当标准的分层贝叶斯方法。我们展示了如何在一个模拟示例中（基于 ProBio 试验）使用我们的方法，在该示例中，我们能够识别出替代指标有用和无用的聚类。一旦 ProBio 试验完成，我们计划将我们的方法应用于该试验。
