论文标题
随机和观察性研究中因果危害比率的灵敏度分析方法
A sensitivity analysis approach for the causal hazard ratio in randomized and observational studies
论文作者
论文摘要
在研究生存数据时，危害比（HR）通常是主要因果作用。尽管它很受欢迎，但人力资源仍以不清楚的因果解释遭受痛苦。正如文献中已经指出的那样，人力资源中存在内置的选择偏见，因为与死亡问题的截断相似，治疗后生存的人力资源条件。最近提出的替代方案是受到幸存者平均因果效应（SACE）的启发，是因果HR，定义为研究参与者之间危害的比率，无论其治疗分配如何，这些参与者都将幸存下来。我们讨论了识别因果人力资源的挑战，并在利用工作脆弱模型的随机对照试验中提出了一种灵敏度分析识别方法。我们进一步扩展了框架，以使用治疗加权的逆概率来调整潜在的混杂因素。我们提出了一个基于COX的基于COX的和灵活的非参数内核估计，并在右审查下提出了基于COX的估计。我们通过模拟研究了提出的估计方法的有限样本特性。我们使用两个真实数据示例说明了我们框架的实用性。
The Hazard Ratio (HR) is often reported as the main causal effect when studying survival data. Despite its popularity, the HR suffers from an unclear causal interpretation. As already pointed out in the literature, there is a built-in selection bias in the HR, because similarly to the truncation by death problem, the HR conditions on post-treatment survival. A recently proposed alternative, inspired by the Survivor Average Causal Effect (SACE), is the causal HR, defined as the ratio between hazards across treatment groups among the study participants that would have survived regardless of their treatment assignment. We discuss the challenge in identifying the causal HR and present a sensitivity analysis identification approach in randomized controlled trials utilizing a working frailty model. We further extend our framework to adjust for potential confounders using inverse probability of treatment weighting. We present a Cox-based and a flexible non-parametric kernel-based estimation under right censoring. We study the finite-sample properties of the proposed estimation method through simulations. We illustrate the utility of our framework using two real-data examples.
