非参数回归间断设计与生存结果
Nonparametric Regression Discontinuity Designs with Survival Outcomes
AI总结：
本文提出非参数方法用于处理生存结果的回归间断设计，解决完全结局数据假设在时间到事件分析中被违反的问题，通过双重鲁棒删失修正，提高效率和稳健性。
摘要：
准实验评估对于生成现实世界因果证据至关重要，补充随机试验的见解。回归间断设计（RDD）是一种准实验设计，可用于估计基于运行变量跨越阈值的治疗因果效应。此类阈值规则在医疗保健中普遍存在，其中预测和预后生物标志物经常指导治疗决策。然而，标准RDD估计器依赖于完整结局数据，这一假设在时间到事件分析中常被违反，因为删失来自失访。为了解决这一问题，我们提出了一种非参数方法，利用双重鲁棒删失修正，并可与现有RDD估计器结合使用。我们的方法能够处理多个生存终点、长期随访时间和生存及删失的协变量依赖变化。我们讨论了该方法在多个应用领域中的相关性，并通过模拟和前列腺组件的前列腺、肺癌、结直肠和卵巢（PLCO）癌症筛查试验展示了其实用性，其中新方法提供了几个优势，包括更高的效率和对遗漏指定的稳健性。我们还开发了一个开源软件包，$\texttt{rdsurvival}$，用于$\texttt{R}$语言。
英文摘要：
Quasi-experimental evaluations are central for generating real-world causal evidence and complementing insights from randomized trials. The regression discontinuity design (RDD) is a quasi-experimental design that can be used to estimate the causal effect of treatments that are assigned based on a running variable crossing a threshold. Such threshold-based rules are ubiquitous in healthcare, where predictive and prognostic biomarkers frequently guide treatment decisions. However, standard RD estimators rely on complete outcome data, an assumption often violated in time-to-event analyses where censoring arises from loss to follow-up. To address this issue, we propose a nonparametric approach that leverages doubly robust censoring corrections and can be paired with existing RD estimators. Our approach can handle multiple survival endpoints, long follow-up times, and covariate-dependent variation in survival and censoring. We discuss the relevance of our approach across multiple areas of applications and demonstrate its usefulness through simulations and the prostate component of the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial where our new approach offers several advantages, including higher efficiency and robustness to misspecification. We have also developed an open-source software package, $\texttt{rdsurvival}$, for the $\texttt{R}$ language.
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