Instrumental variables regression is a tool that is commonly used in the analysis of observational data. The instrumental variables are used to make causal inference about the effect of a certain exposure in the presence of unmeasured confounders. A valid instrumental variable is a variable that is associated with the exposure, affects the outcome only through the exposure (exclusion criterion), and is not confounded with the outcome (exogeneity). These assumptions are generally untestable and rely on subject-matter knowledge. Therefore, a sensitivity analysis is desirable to assess the impact of assumptions violation on the estimated parameters. In this paper, we propose and demonstrate a new method of sensitivity analysis for G-estimators in causal linear and non-linear models. We introduce two novel aspects of sensitivity analysis in instrumental variables studies. The first is a single sensitivity parameter that captures violations of exclusion and exogeneity assumptions. The second is an application of the method to non-linear models. The introduced framework is theoretically justified and is illustrated via a simulation study. Finally, we illustrate the method by application to real-world data and provide practitioners with guidelines on conducting sensitivity analysis.
翻译:仪器变量回归是分析观测数据时常用的工具。工具变量用于在没有测量的折叠者在场的情况下对某种暴露的影响作出因果推断。有效的工具变量是与暴露相关的变量,仅通过暴露影响结果(排除标准),不与结果(外异性)混为一谈。这些假设一般是无法测试的,并依赖主题事项知识。因此,敏感度分析是评估假设违反估计参数的影响的可取方法。在本文中,我们提议并展示了一种在因果线性和非线性模型中为G-估计数值进行敏感度分析的新方法。我们在工具变量研究中引入了敏感度分析的两个新颖方面。第一个是单一敏感度参数,捕捉违反排斥和外异性假设的情况。第二个是将这种方法应用于非线性模型。引入的框架在理论上是有道理的,并通过模拟研究加以说明。最后,我们通过应用真实世界数据来说明方法,并向从业人员提供进行敏感度分析的指导方针。