Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.
Epidemiology. 2023 Sep 1;34(5):673-680. doi: 10.1097/EDE.0000000000001635. Epub 2023 May 30.
Misclassification bias is a common concern in epidemiologic studies. Despite strong bias on main effects, gene-environment interactions have been shown to be biased towards the null under gene-environment independence. In the context of a recent article examining the interaction between nerve agent exposure and paraoxonase-1 gene on Gulf War Illness, we aimed to assess the impact of recall bias-a common misclassfication bias-on the identification of gene-environment interactions when the independence assumption is violated.
We derive equations to quantify the bias of the interaction, and numerically illustrate these results by simulating a case-control study of 1000 cases and 1000 controls. Simulation input parameters included exposure prevalence, strength of gene-environment dependence, strength of the main effect, exposure specificity among cases, and strength of the gene-environment interaction.
We show that, even if gene-environment independence is violated, we can bound possible gene-environment interactions by knowing the strength and direction of the gene-environment dependence ( ) and the observed gene-environment interaction ( )-thus often still allowing for the identification of such interactions. Depending on whether is larger or smaller than the inverse of , is a lower (if ) or upper (if ) bound for the true interaction. In addition, the bias magnitude is somewhat predictable by examining other characteristics such as exposure prevalence, the strength of the exposure main effect, and directions of the recall bias and gene-environment dependence.
Even if gene-environment dependence exists, we may still be able to identify gene-environment interactions even when misclassification bias is present.
在流行病学研究中,分类偏倚是一个常见的问题。尽管主要效应存在很强的偏倚,但在基因-环境独立性假设下,基因-环境相互作用已被证明偏向于零。在最近一篇研究神经毒剂暴露与海湾战争病中对氧磷酶 1 基因相互作用的文章中,我们旨在评估在违反独立性假设时,回忆偏倚(一种常见的分类偏倚)对识别基因-环境相互作用的影响。
我们推导出量化相互作用偏倚的方程,并通过模拟一个包含 1000 例病例和 1000 例对照的病例对照研究来数值说明这些结果。模拟输入参数包括暴露流行率、基因-环境依赖性强度、主要效应强度、病例中暴露的特异性以及基因-环境相互作用强度。
我们表明,即使违反了基因-环境独立性,我们也可以通过了解基因-环境依赖性的强度和方向()和观察到的基因-环境相互作用()来限制可能的基因-环境相互作用-因此通常仍然允许识别这些相互作用。取决于是否大于或小于的倒数,是真实相互作用的下限(如果)或上限(如果)。此外,通过检查其他特征,如暴露流行率、暴露主要效应的强度以及回忆偏倚和基因-环境依赖性的方向,偏倚幅度在一定程度上是可预测的。
即使存在基因-环境依赖性,即使存在分类偏倚,我们也可能仍然能够识别基因-环境相互作用。