Hazard ratios are frequently reported in time-to-event and epidemiological studies to assess treatment effects. In observational studies, the combination of propensity score weights with the Cox proportional hazards model facilitates the estimation of the marginal hazard ratio (MHR). The methods for estimating MHR are analogous to those employed for estimating common causal parameters, such as the average treatment effect. However, MHR estimation in the context of high-dimensional data remain unexplored. This paper seeks to address this gap through a simulation study that consider variable selection methods from causal inference combined with a recently proposed multiply robust approach for MHR estimation. Additionally, a case study utilizing stroke register data is conducted to demonstrate the application of these methods. The results from the simulation study indicate that the double selection covariate selection method is preferable to several other strategies when estimating MHR. Nevertheless, the estimation can be further improved by employing the multiply robust approach to the set of propensity score models obtained during the double selection process.

翻译：风险比（Hazard Ratios）在生存分析和流行病学研究中常被用于评估治疗效果。在观察性研究中，倾向性评分加权与Cox比例风险模型的结合有助于估计边际风险比（MHR）。估计MHR的方法与估计常见因果参数（如平均处理效应）的方法类似。然而，高维数据背景下MHR的估计尚未得到充分探索。本文旨在通过模拟研究弥补这一空白，该研究结合了因果推断中的变量选择方法与最新提出的多重稳健MHR估计方法。此外，利用卒中登记数据开展案例研究，以展示这些方法的应用。模拟研究结果表明，在估计MHR时，双重选择协变量方法优于其他几种策略。然而，通过将多重稳健方法应用于双重选择过程中获得的倾向性评分模型集合，可以进一步改进估计效果。