Jackson John W, Swanson Sonja A
Harvard T.H. Chan School of Public Health, Department of Epidemiology, Boston, MA.
Brigham and Women's Hospital, Department of Medicine, Division of Pharmacoepidemiology and Pharmacoeconomics, Boston, MA.
Epidemiology. 2015 Jul;26(4):498-504. doi: 10.1097/EDE.0000000000000287.
Recommendations for reporting instrumental variable analyses often include presenting the balance of covariates across levels of the proposed instrument and levels of the treatment. However, such presentation can be misleading as relatively small imbalances among covariates across levels of the instrument can result in greater bias because of bias amplification. We introduce bias plots and bias component plots as alternative tools for understanding biases in instrumental variable analyses. Using previously published data on proposed preference-based, geography-based, and distance-based instruments, we demonstrate why presenting covariate balance alone can be problematic, and how bias component plots can provide more accurate context for bias from omitting a covariate from an instrumental variable versus non-instrumental variable analysis. These plots can also provide relevant comparisons of different proposed instruments considered in the same data. Adaptable code is provided for creating the plots.
关于报告工具变量分析的建议通常包括展示在所提出的工具变量各水平以及治疗各水平上协变量的平衡性。然而，这样的展示可能会产生误导，因为由于偏差放大，工具变量各水平间协变量相对较小的不平衡可能会导致更大的偏差。我们引入偏差图和偏差成分图作为理解工具变量分析中偏差的替代工具。利用先前发表的关于基于偏好、基于地理和基于距离的工具变量的数据，我们证明了仅展示协变量平衡为何会有问题，以及偏差成分图如何能为在工具变量分析与非工具变量分析中遗漏协变量所导致的偏差提供更准确的背景信息。这些图还能对同一数据中考虑的不同工具变量提议进行相关比较。文中提供了用于创建这些图的可改编代码。
