Effects of Adjusting for Instrumental Variables on Bias and Precision of Effect Estimates
Effects of Adjusting for Instrumental Variables on Bias and Precision of Effect Estimates
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DOI：
10.1093/aje/kwr364
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发表时间：
2011-12-01
影响因子：
5
通讯作者：
Glynn, Robert J.
中科院分区：
文献类型：
--
作者：
Myers, Jessica A.;Rassen, Jeremy A.;Glynn, Robert J.
最近的理论研究表明，条件作用于一个工具变量（IV），一个与暴露相关但与结果无关的变量，除了通过暴露，可以增加暴露效应估计的偏差和方差。尽管这些发现在已知IV的情况下具有明显的意义，但在更常见的情况下，其意义仍不清楚，即研究者不确定测量的协变量是否符合IV的标准或更确切地说是混杂因素。作者介绍了两项模拟研究的结果，旨在深入了解在常规流行病学实践中对潜在IV进行调节的问题。模拟探讨了条件反射对IV、近IV（与结果弱相关的暴露预测因子）以及混杂因素对二元暴露效应估计的偏倚和方差的影响。结果表明，以完美IV或接近IV为条件的效应估计可能比无条件估计具有更大的偏差和方差。然而，在大多数情况下考虑，由于条件误差的增加是小的总估计误差相比。在这些情况下，在选择调整变量时，应优先考虑最大限度地减少未测量的混杂因素，即使有IV调节的风险。
Recent theoretical studies have shown that conditioning on an instrumental variable (IV), a variable that is associated with exposure but not associated with outcome except through exposure, can increase both bias and variance of exposure effect estimates. Although these findings have obvious implications in cases of known IVs, their meaning remains unclear in the more common scenario where investigators are uncertain whether a measured covariate meets the criteria for an IV or rather a confounder. The authors present results from two simulation studies designed to provide insight into the problem of conditioning on potential IVs in routine epidemiologic practice. The simulations explored the effects of conditioning on IVs, near-IVs (predictors of exposure that are weakly associated with outcome), and confounders on the bias and variance of a binary exposure effect estimate. The results indicate that effect estimates which are conditional on a perfect IV or near-IV may have larger bias and variance than the unconditional estimate. However, in most scenarios considered, the increases in error due to conditioning were small compared with the total estimation error. In these cases, minimizing unmeasured confounding should be the priority when selecting variables for adjustment, even at the risk of conditioning on IVs.