When direct measurement of a clinically relevant primary endpoint in a clinical trial is infeasible, a surrogate endpoint may be used instead to infer treatment effects. Trial-level surrogates predict the average treatment effect on the primary endpoint and may be evaluated within the meta-analytic framework. However, traditional methods are ill-suited to the complex high-dimensional data now increasingly collected in modern trials, such as omics data. Although methods for high-dimensional surrogate evaluation exist, they have largely been developed for single-trial settings and therefore cannot assess surrogate generalisability. Here, we propose RISE-Meta, an approach for evaluating trial-level surrogate markers in the multi-trial, high-dimensional setting. In the first stage, an existing nonparametric method is applied to individual participant data to derive study-level surrogacy metrics for each candidate marker. Next, random-effects meta-analysis combines these metrics across studies, and equivalence testing provides operational criteria for surrogate validity. Finally, a subset of candidates is combined into a composite signature through a weighting scheme to improve surrogacy relative to any individual candidate. We evaluate RISE-Meta in both simulation studies and real data applications. In an application to high-dimensional data, we analyse gene expression as trial-level surrogate markers for the antibody response to seasonal influenza vaccination, while in a low-dimensional application we compare RISE-Meta to a reference meta-analytic approach and observe strong agreement between the two.

翻译：当临床试验中无法直接测量具有临床相关性的主要终点时，可使用替代终点间接推断治疗效果。试验级替代标志物用于预测主要终点的平均治疗效果，并可通过荟萃分析框架进行评估。然而，传统方法难以适应现代临床试验中日益收集的复杂高维数据（如组学数据）。尽管现有高维替代标志物评估方法，但其主要针对单试验场景开发，因此无法评估替代标志物的泛化能力。本文提出RISE-Meta方法，用于在多试验、高维场景下评估试验级替代标志物。第一阶段，对个体参与者数据应用现有非参数方法，推导每个候选标志物的研究级替代性指标。第二阶段，通过随机效应荟萃分析整合各研究指标，并采用等效性检验提供替代有效性的操作化标准。最终，通过加权方案将部分候选标志物组合为复合特征，以提升相对单一候选标志物的替代性。我们通过模拟研究与真实数据应用对RISE-Meta进行评估。在高维数据应用中，我们分析基因表达作为季节性流感疫苗抗体应答的试验级替代标志物；在低维应用场景中，我们将RISE-Meta与参考荟萃分析方法进行比较，观察到两者高度一致。