Department of Population Health, 12296New York University, New York, NY, USA.
Stat Methods Med Res. 2023 Mar;32(3):524-538. doi: 10.1177/09622802221146310. Epub 2023 Jan 12.
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.
协变量平衡在观察性研究中获得无偏治疗效果估计至关重要。已经成功提出了针对协变量平衡的方法，并广泛应用于估计连续结局的治疗效果。然而，在许多医学和流行病学应用中，人们的兴趣在于估计治疗对事件时间结局的效果。对于这种类型的数据，最常见的感兴趣的估计量之一是 Cox 比例风险模型的边际风险比。在本文中，我们首先提出了稳健正交权重，这是一组通过解决二次约束优化问题得到的权重，该问题最大化了精度，同时约束了协变量平衡，定义为混杂因素与治疗之间的相关性。通过这种方式，稳健正交权重可以最优地处理二分类和连续治疗。然后，我们在一项模拟研究中评估了所提出的权重在估计二分类和连续治疗的边缘风险比的性能，这些治疗与事件时间结局相关。最后，我们在 Women's Health Initiative 观察性研究中，应用稳健正交权重评估激素治疗对冠心病时间的影响以及红肉摄入对 24069 名绝经后妇女结肠癌时间的影响。
