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 works. Besides, 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. 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 competitors 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.
翻译:Cox回归模型及其相关的危害比率(HR)经常用来总结治疗及时与事件结果发生后的效果。然而,HR的解释在很大程度上取决于假定的基本生存模型。解释HR的挑战一直是最近一些工作的重点。此外,为了处理这些关注问题,还提出了几项替代措施。边际Cox回归模型包括一个可识别的危险比率,没有个别但因人口因素造成的因果解释。在这项工作中,我们研究一个特定的边际Cox回归模型的特性,并在遗漏者在场的情况下考虑其估计。我们证明,允许非二元治疗的估计得分具有很大的样本一致性。我们蒙特卡洛的模拟表明,该程序的有限抽样行为是充分的。所研究的估算值比其较弱工具的竞争者更有力,尽管它对于治疗的重大影响略有偏差。所介绍的技术的实际使用通过一个实际实例,用血管质量倡议登记册的数据加以说明。所使用的R代码作为补充材料提供。