Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA.
Stat Med. 2012 Sep 28;31(22):2552-64. doi: 10.1002/sim.4354. Epub 2011 Oct 4.
We develop a sensitivity analysis technique to assess the sensitivity of interaction analyses to unmeasured confounding. We give bias formulas for sensitivity analysis for interaction under unmeasured confounding on both additive and multiplicative scales. We provide simplified formulas in the case in which either one of the two factors does not interact with the unmeasured confounder in its effects on the outcome. An interesting consequence of the results is that if the two exposures of interest are independent (e.g., gene-environment independence), even under unmeasured confounding, if the estimate of the interaction is nonzero, then either there is a true interaction between the two factors or there is an interaction between one of the factors and the unmeasured confounder; an interaction must be present in either scenario. We apply the results to two examples drawn from the literature.
我们开发了一种敏感性分析技术，以评估对未测量混杂的相互作用分析的敏感性。我们给出了在未测量混杂情况下，基于加性和乘法尺度的相互作用敏感性分析的偏差公式。在两种因素之一与未测量混杂因素在其对结果的影响中不相互作用的情况下，我们提供了简化的公式。结果的一个有趣结果是，如果两个感兴趣的暴露是独立的（例如，基因-环境独立性），即使在未测量混杂的情况下，如果相互作用的估计值不为零，那么两个因素之间必然存在真正的相互作用，或者一个因素与未测量混杂因素之间存在相互作用；在这两种情况下都必须存在相互作用。我们将结果应用于文献中提出的两个例子。
