Canan Chelsea, Lesko Catherine, Lau Bryan
From the Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Epidemiology. 2017 May;28(3):396-398. doi: 10.1097/EDE.0000000000000639.
Instrumental variables (IV) are used to draw causal conclusions about the effect of exposure E on outcome Y in the presence of unmeasured confounders. IV assumptions have been well described: (1) IV affects E; (2) IV affects Y only through E; (3) IV shares no common cause with Y. Even when these assumptions are met, biased effect estimates can result if selection bias allows a noncausal path from E to Y. We demonstrate the presence of bias in IV analyses on a sample from a simulated dataset, where selection into the sample was a collider on a noncausal path from E to Y. By applying inverse probability of selection weights, we were able to eliminate the selection bias. IV approaches may protect against unmeasured confounding but are not immune from selection bias. Inverse probability of selection weights used with IV approaches can minimize bias.
在存在未测量混杂因素的情况下，工具变量（IV）用于得出关于暴露因素E对结局Y影响的因果结论。IV假设已得到充分描述：（1）IV影响E；（2）IV仅通过E影响Y；（3）IV与Y没有共同的原因。即使满足这些假设，如果选择偏倚允许从E到Y的非因果路径，也可能导致有偏差的效应估计。我们在一个模拟数据集的样本上证明了IV分析中存在偏差，其中进入样本的选择是从E到Y的非因果路径上的一个对撞机。通过应用选择权重的逆概率，我们能够消除选择偏倚。IV方法可能可以防范未测量的混杂，但不能免受选择偏倚的影响。与IV方法一起使用的选择权重逆概率可以将偏差最小化。
