论文标题
锐化Rosenbaum灵敏度范围，以解决对观察到的与未观察到的协变量之间相互作用的关注
Sharpening the Rosenbaum Sensitivity Bounds to Address Concerns About Interactions Between Observed and Unobserved Covariates
论文作者
论文摘要
在观察性研究中，假设治疗是随机分配的，甚至是为了调整所有观察到的协变量的条件，通常是不现实的。因此，通常需要进行灵敏度分析来检查由于未观察到的协变量引起的隐藏偏见会影响治疗效果的推论。在匹配的观察性研究中，每个处理的单元与观察到的协变量匹配的一个或多个未处理的对照，Rosenbaum边界灵敏度分析是最流行的灵敏度分析模型之一。在本文中，我们表明，在观察到的协变量和未观察到的协变量之间存在相互作用的情况下，直接应用Rosenbaum界限几乎不可避免地夸大了因果关系对隐藏偏见的敏感性的报道。我们给出更尖锐的优势比范围来解决此缺陷。我们通过研究愤怒/敌意倾向对患有心脏问题的风险的影响来说明我们的新方法。
In observational studies, it is typically unrealistic to assume that treatments are randomly assigned, even conditional on adjusting for all observed covariates. Therefore, a sensitivity analysis is often needed to examine how hidden biases due to unobserved covariates would affect inferences on treatment effects. In matched observational studies where each treated unit is matched to one or multiple untreated controls for observed covariates, the Rosenbaum bounds sensitivity analysis is one of the most popular sensitivity analysis models. In this paper, we show that in the presence of interactions between observed and unobserved covariates, directly applying the Rosenbaum bounds will almost inevitably exaggerate the report of sensitivity of causal conclusions to hidden bias. We give sharper odds ratio bounds to fix this deficiency. We illustrate our new method through studying the effect of anger/hostility tendency on the risk of having heart problems.
