Surrogate markers are often employed in clinical trials to replace primary outcomes that may be difficult, expensive, or time-consuming to measure directly. These markers can accelerate the evaluation of new treatments, provided they reliably capture the causal relationship between treatment and true clinical benefit. Parast et al. (2024) recently proposed a rank-based approach for evaluating surrogate markers, characterized by its nonparametric nature and minimal assumptions. While this method is useful in small-sample model-agnostic settings, it has several limitations, including a lack of clear causal interpretation, low statistical power, and insufficient robustness to different data-generating mechanisms. In this paper, we propose a Bayesian approach that addresses these shortcomings by focusing on causal treatment effect estimands and, in doing so, improves power through covariate adjustment. We demonstrate the advantages of our proposed method through a simulation study designed to highlight gains in both accuracy and power.

翻译：替代标志物在临床试验中常被用于替代那些难以直接测量、成本高昂或耗时过长的首要结局指标。只要这些标志物能够可靠地捕捉治疗与真实临床获益之间的因果关系，它们就能加速新疗法的评估进程。Parast等人（2024）近期提出了一种基于秩的替代标志物评估方法，其特点在于非参数性质及对假设条件的最低要求。尽管该方法在小样本且模型无关的场景中具有一定实用性，但它存在若干局限性，包括缺乏明确的因果解释、统计功效较低以及对不同数据生成机制的鲁棒性不足。本文提出一种贝叶斯方法，通过聚焦于因果处理效应的估计量来应对上述缺陷，并借助协变量调整提升统计功效。我们通过一项模拟研究展示了所提方法的优势，该研究旨在突显其在准确性与统计功效两方面的提升。