Survival analysis plays a crucial role in understanding time-to-event (survival) outcomes such as disease progression. Despite recent advancements in causal mediation frameworks for survival analysis, existing methods are typically based on Cox regression and primarily focus on a single exposure or individual omics layers, often overlooking multi-omics interplay. This limitation hinders the full potential of integrated biological insights. In this paper, we propose SMAHP, a novel method for survival mediation analysis that simultaneously handles high-dimensional exposures and mediators, integrates multi-omics data, and offers a robust statistical framework for identifying causal pathways on survival outcomes. This is one of the first attempts to introduce the accelerated failure time (AFT) model within a multi-omics causal mediation framework for survival outcomes. Through simulations across multiple scenarios, we demonstrate that SMAHP achieves high statistical power, while effectively controlling false discovery rate (FDR), compared with two other approaches. We further apply SMAHP to the largest head-and-neck carcinoma proteogenomic data, detecting a gene mediated by a protein that influences survival time.

翻译：生存分析在理解疾病进展等时间-事件（生存）结局中起着关键作用。尽管近期生存分析的因果中介框架取得了进展，但现有方法通常基于Cox回归，主要关注单一暴露或单一组学层面，往往忽略了多组学间的相互作用。这一局限阻碍了整合生物学见解的充分发挥。本文提出SMAHP，这是一种新颖的生存中介分析方法，能够同时处理高维暴露与中介变量，整合多组学数据，并为识别生存结局的因果路径提供稳健的统计框架。这是在多组学因果中介框架中为生存结局引入加速失效时间（AFT）模型的首批尝试之一。通过多种场景的模拟，我们证明与其他两种方法相比，SMAHP在有效控制错误发现率（FDR）的同时，获得了较高的统计功效。我们进一步将SMAHP应用于最大的头颈癌蛋白质组学数据，检测到一个通过蛋白质介导并影响生存时间的基因。