协变量平衡对于在观察研究中获得无偏估计的治疗效应至关重要。针对协变量平衡的方法已被成功地提出，并大量应用于连续结局的治疗效应的估计。然而，在许多医学和流行病学应用中，我们感兴趣的是评估治疗对事件发生时间的影响。对于这种类型的数据，一个最常见的感兴趣的评估量是Cox比例风险模型的边际危险比。在本文中，我们首先介绍强健的正交权重——通过解决一个二次约束优化问题获得的一组权重，这个问题在限制协变量的平衡(定义为混淆因素和治疗的相关性)的同时最大化精度。通过这样做，强健的正交权重能够最优地处理二元治疗和连续治疗。然后，我们对所提出的权重在模拟研究中估计二元治疗和连续治疗对事件时间的边际危险比的表现进行评估。最后，我们应用强健的正交权重在24,069名参加女性健康计划观测研究的绝经后妇女中，评估荷尔蒙治疗对冠心病时间和红肉消耗对结肠癌时间的影响。

Covariate balance is crucial in obtaining unbiased estimates of treatment effects in observational studies. Methods that target covariate balance have been successfully proposed and largely applied to estimate treatment effects on continuous outcomes. However, in many medical and epidemiological applications, the interest lies in estimating treatment effects on time-to-event outcomes. With this type of data, one of the most common estimands of interest is the marginal hazard ratio of the Cox proportional hazards model. In this article, we start by presenting robust orthogonality weights, a set of weights obtained by solving a quadratic constrained optimization problem that maximizes precision while constraining covariate balance defined as the correlation between confounders and treatment. By doing so, robust orthogonality weights optimally deal with both binary and continuous treatments. We then evaluate the performance of the proposed weights in estimating marginal hazard ratios of binary and continuous treatments with time-to-event outcomes in a simulation study. We finally apply robust orthogonality weights in the evaluation of the effect of hormone therapy on time to coronary heart disease and on the effect of red meat consumption on time to colon cancer among 24,069 postmenopausal women enrolled in the Women's Health Initiative observational study.