Abstract

In observational studies, identification of ATEs is generally achieved by assuming that the correct set of confounders has been measured and properly included in the relevant models. Because this assumption is both strong and untestable, a sensitivity analysis should be performed. Common approaches include modeling the bias directly or varying the propensity scores to probe the effects of a potential unmeasured confounder. In this article, we take a novel approach whereby the sensitivity parameter is the “proportion of unmeasured confounding”: the proportion of units for whom the treatment is not as good as randomized even after conditioning on the observed covariates. We consider different assumptions on the probability of a unit being unconfounded. In each case, we derive sharp bounds on the average treatment effect as a function of the sensitivity parameter and propose nonparametric estimators that allow flexible covariate adjustment. We also introduce a one-number summary of a study’s robustness to the number of confounded units. Finally, we explore finite-sample properties via simulation, and apply the methods to an observational database used to assess the effects of right heart catheterization. Supplementary materials for this article are available online.

摘要

在观察性研究中，通常通过假设已测量正确的混杂因素集并将其正确包含在相关模型中来识别 ATE。因为这个假设既强大又无法检验，因此应该进行敏感性分析。常见的方法包括直接对偏差建模或改变倾向得分以探测潜在的未测量混杂因素的影响。在本文中，我们采用了一种新颖的方法，其中灵敏度参数是“未测量混杂的比例”：即使在对观察到的协变量进行调节后，治疗效果不如随机化的单位比例。我们考虑了关于一个单元未被混淆的概率的不同假设。在每种情况下，我们得出了作为灵敏度参数函数的平均治疗效果的明确界限，并提出了允许灵活协变量调整的非参数估计器。我们还介绍了一项研究对混杂单位数量的稳健性的单数总结。最后，我们通过模拟探索有限样本特性，并将这些方法应用于用于评估右心导管插入术效果的观察数据库。本文的补充材料可在线获取。并将这些方法应用于用于评估右心导管插入术效果的观察数据库。本文的补充材料可在线获取。并将这些方法应用于用于评估右心导管插入术效果的观察数据库。本文的补充材料可在线获取。