Instrumental variable analysis of multiplicative models with potentially invalid instruments
Instrumental variable analysis of multiplicative models with potentially invalid instruments
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DOI：
10.1002/sim.7069
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发表时间：
2016-12-01
影响因子：
2
通讯作者：
Ferrucci, Luigi
中科院分区：
文献类型：
--
作者：
Shardell, Michelle;Ferrucci, Luigi
工具变量（IV）方法有可能在存在未测量混杂因素的情况下一致地估计暴露对结果的因果影响。然而，IV方法的有效性依赖于强有力的假设，其中一些无法从观测数据中得到最终验证。一个这样的假设是，所提出的工具对结果的影响完全由暴露介导。我们考虑的情况是，这一假设被违反，但其余的IV假设仍然成立;即，拟议的IV（1）与暴露有关，（2）与结果没有共同的不可测量的原因。我们提出了一种方法来估计乘法结构平均模型的二进制结果在这种情况下存在不可测量的混杂。我们还扩展了该方法以处理多个场景，包括中介分析。该方法采用了渐近有效的G-估计方法，以前提出的加性结构平均模型，它可以使用现成的软件进行广义矩量法。蒙特卡罗仿真研究表明，该方法具有低偏差和准确覆盖的特点。我们应用该方法的循环维生素D和抑郁症状的病例研究，使用季节的血液采集作为（可能无效）的工具变量。所提出的方法的潜在应用包括随机干预研究以及孟德尔随机化研究，其中遗传变异影响多种表型，可能包括结果。2016年出版。本条目属于美国政府作品，在美国属于公有领域。
Instrumental variable (IV) methods have potential to consistently estimate the causal effect of an exposure on an outcome in the presence of unmeasured confounding. However, validity of IV methods relies on strong assumptions, some of which cannot be conclusively verified from observational data. One such assumption is that the effect of the proposed instrument on the outcome is completely mediated by the exposure. We consider the situation where this assumption is violated, but the remaining IV assumptions hold; that is, the proposed IV (1) is associated with the exposure and (2) has no unmeasured causes in common with the outcome. We propose a method to estimate multiplicative structural mean models of binary outcomes in this scenario in the presence of unmeasured confounding. We also extend the method to address multiple scenarios, including mediation analysis. The method adapts the asymptotically efficient G-estimation approach that was previously proposed for additive structural mean models, and it can be carried out using off-the-shelf software for generalized method of moments. Monte Carlo simulation studies show that the method has low bias and accurate coverage. We applied the method to a case study of circulating vitamin D and depressive symptoms using season of blood collection as a (potentially invalid) instrumental variable. Potential applications of the proposed method include randomized intervention studies as well as Mendelian randomization studies with genetic variants that affect multiple phenotypes, possibly including the outcome. Published 2016. This article is a U.S. Government work and is in the public domain in the USA