Assessing Sensitivity to Unmeasured Confounding Using a Simulated Potential Confounder
Assessing Sensitivity to Unmeasured Confounding Using a Simulated Potential Confounder
复制标题
DOI：
10.1080/19345747.2015.1078862
复制
发表时间：
2016-01-01
影响因子：
1.8
通讯作者：
Hill, Jennifer L.
中科院分区：
文献类型：
--
作者：
Carnegie, Nicole Bohme;Harada, Masataka;Hill, Jennifer L.
发展以证据为基础的政策的一个主要障碍是难以实施随机试验来回答所有感兴趣的因果问题。当使用非实验研究时，评估未测量的混杂对结果的影响程度是至关重要的。我们提出了一套图形和数字工具来探索因果估计对未测量混杂因素存在的敏感性。我们通过描述(A)混杂因素和治疗分配之间的关系以及(B)混杂因素和结果变量之间的关系的两个参数来表征混杂因素。与目前在标准软件中实现的类似方法相比，我们的方法有两个主要优势。首先，它既可以应用于连续变量，也可以应用于二元处理变量。其次，我们对二元处理变量的方法允许研究人员指定三个可能的标准(平均治疗效果、治疗对受试者的影响、治疗对对照的影响)。这些选项都在一个名为TreatSens的R包中实现。通过仿真验证了该方法的有效性。我们通过两个策略应用说明了它在实践中的潜在用处。
A major obstacle to developing evidenced-based policy is the difficulty of implementing randomized experiments to answer all causal questions of interest. When using a nonexperimental study, it is critical to assess how much the results could be affected by unmeasured confounding. We present a set of graphical and numeric tools to explore the sensitivity of causal estimates to the presence of an unmeasured confounder. We characterize the confounder through two parameters that describe the relationships between (a) the confounder and the treatment assignment and (b) the confounder and the outcome variable. Our approach has two primary advantages over similar approaches that are currently implemented in standard software. First, it can be applied to both continuous and binary treatment variables. Second, our method for binary treatment variables allows the researcher to specify three possible estimands (average treatment effect, effect of the treatment on the treated, effect of the treatment on the controls). These options are all implemented in an R package called treatSens. We demonstrate the efficacy of the method through simulations. We illustrate its potential usefulness in practice in the context of two policy applications.