Department of Anesthesiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA.
Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
Int J Biostat. 2022 Mar 23;19(1):39-52. doi: 10.1515/ijb-2021-0096. eCollection 2023 May 1.
The Cox regression model and its associated hazard ratio (HR) are frequently used for summarizing the effect of treatments on time to event outcomes. However, the HR's interpretation strongly depends on the assumed underlying survival model. The challenge of interpreting the HR has been the focus of a number of recent papers. Several alternative measures have been proposed in order to deal with these concerns. The marginal Cox regression models include an identifiable hazard ratio without individual but populational causal interpretation. In this work, we study the properties of one particular marginal Cox regression model and consider its estimation in the presence of omitted confounder from an instrumental variable-based procedure. We prove the large sample consistency of an estimation score which allows non-binary treatments. Our Monte Carlo simulations suggest that finite sample behavior of the procedure is adequate. The studied estimator is more robust than its competitor (Wang et al.) for weak instruments although it is slightly more biased for large effects of the treatment. The practical use of the presented techniques is illustrated through a real practical example using data from the vascular quality initiative registry. The used R code is provided as Supplementary material.
Cox 回归模型及其相关的风险比（HR）常用于总结治疗对事件时间结局的影响。然而，HR 的解释强烈依赖于假设的潜在生存模型。解释 HR 的挑战一直是最近一些论文的重点。为了解决这些问题，已经提出了几种替代的度量方法。边缘 Cox 回归模型包括一个可识别的风险比，没有个体但有群体因果解释。在这项工作中，我们研究了一个特定的边缘 Cox 回归模型的性质，并考虑了在存在工具变量的情况下，从一个基于工具变量的程序中遗漏混杂因素的估计。我们证明了一个允许非二进制处理的估计分数的大样本一致性。我们的蒙特卡罗模拟表明，该程序的有限样本行为是足够的。对于弱工具变量，所研究的估计量比其竞争对手（Wang 等人）更稳健，尽管对于治疗效果较大时，它的偏差略大。通过使用血管质量倡议登记处的数据的实际示例说明了所提出技术的实际应用。所使用的 R 代码作为补充材料提供。
