Instrumental variable analysis with a nonlinear exposure-outcome relationship.
Instrumental variable analysis with a nonlinear exposure-outcome relationship.
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DOI：
10.1097/ede.0000000000000161
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发表时间：
2014-11
期刊：
影响因子：
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通讯作者：
EPIC-InterAct Consortium
中科院分区：
文献类型：
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作者：
Burgess S;Davies NM;Thompson SG;EPIC-InterAct Consortium
补充数字内容可在正文中找到。工具变量方法可以使用观测数据来估计暴露对结果的因果影响。许多工具变量方法假设暴露-结果关系是线性的，但在实践中，这一假设往往是值得怀疑的，或者可能这种关系的形状是研究的目标。我们在孟德尔随机化的背景下研究这个问题，使用遗传变异作为工具变量。通过模拟，我们证明了当曝光-结果关系的真实形状不是线性时，简单的线性辅助变量方法的性能。我们还提出了一种新的方法来估计暴露在暴露分布的各层内对结果的影响。这使得能够使用滑动窗口方法来估计暴露的分位数组内的局部平均因果效应或作为暴露的连续函数。我们的模拟表明，线性工具变量估计近似于总体平均因果效应。这是如果人群中每个人的暴露剂量增加固定数量时结果的平均差异。对局部平均因果效应的估计揭示了各种模型的暴露-结果关系的形状。这些方法被用来研究体重指数与一系列心血管危险因素之间的关系。非线性暴露-结果关系不应成为工具变量分析的障碍。当暴露-结果关系不是线性时，既可以估计总体平均因果效应，也可以估计暴露-结果关系的形状。
Supplemental Digital Content is available in the text. Instrumental variable methods can estimate the causal effect of an exposure on an outcome using observational data. Many instrumental variable methods assume that the exposure–outcome relation is linear, but in practice this assumption is often in doubt, or perhaps the shape of the relation is a target for investigation. We investigate this issue in the context of Mendelian randomization, the use of genetic variants as instrumental variables. Using simulations, we demonstrate the performance of a simple linear instrumental variable method when the true shape of the exposure–outcome relation is not linear. We also present a novel method for estimating the effect of the exposure on the outcome within strata of the exposure distribution. This enables the estimation of localized average causal effects within quantile groups of the exposure or as a continuous function of the exposure using a sliding window approach. Our simulations suggest that linear instrumental variable estimates approximate a population-averaged causal effect. This is the average difference in the outcome if the exposure for every individual in the population is increased by a fixed amount. Estimates of localized average causal effects reveal the shape of the exposure–outcome relation for a variety of models. These methods are used to investigate the relations between body mass index and a range of cardiovascular risk factors. Nonlinear exposure–outcome relations should not be a barrier to instrumental variable analyses. When the exposure–outcome relation is not linear, either a population-averaged causal effect or the shape of the exposure–outcome relation can be estimated.