针对生存结局的未观测混杂双有效且双尖锐敏感性分析
Doubly valid and doubly sharp sensitivity analysis to unobserved confounding for survival outcomes
- Sanofi R&D（赛诺菲研发）
- Inria, Inserm, Université Paris Cité, HeKA（法国国家信息与自动化研究所，法国健康与医学研究院，巴黎西岱大学，HeKA）
- CIC-EC 1418 - Paris HEGP（巴黎HEGP医院-研究中心1418临床转化中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对生存结局，提出基于边际敏感性模型的双有效且双尖锐界，用于敏感性分析未观测混杂，涵盖生存函数差异和RMST，在模拟和真实数据上比先前方法更紧且更高效。
AI中文摘要：
时间-事件结局在肿瘤学和罕见病中至关重要，其中治疗效果通常通过生存曲线差异或限制平均生存时间（RMST）来总结。在真实世界数据中，估计这些因果效应依赖于不存在未观测混杂的假设，而该假设很少得到满足。我们开发了一个在边际敏感性模型（MSM）下针对生存结局的因果治疗效果的敏感性分析框架。我们引入了生存函数差异和RMST的双有效且双尖锐（DVDS）界，将近期DVDS结果扩展到时间-事件设置，同时考虑信息性删失。在实践中，与文献中的先前方法相比，我们的方法在模拟和真实数据上产生更紧的界并提高计算效率。为了可处理性，我们假设删失与未观测混杂之间独立，这一限制应在未来工作中解决。
英文摘要：
Time-to-event outcomes are central in oncology and rare diseases, where treatment effects are often summarized by differences in survival curves or Restricted Mean Survival Time (RMST). In real-world data, estimating these causal effects relies on the absence of unobserved confounding, an assumption that is rarely satisfied. We develop a sensitivity analysis framework for causal treatment effects with survival outcomes under the Marginal Sensitivity Model (MSM). We introduce doubly valid and doubly sharp (DVDS) bounds for differences in survival functions and RMST, extending recent DVDS results to the time-to-event setting while accounting for informative censoring. In practice, our method yields tighter bounds and improved computational efficiency compared to a previous approach from the literature, on simulated and real data. For tractability, we assume independence between censoring and unobserved confounding, a limit that should be addressed in future works.
