Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California.
Am J Epidemiol. 2018 Feb 1;187(2):366-377. doi: 10.1093/aje/kwx243.
There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.
最近有人提议，采用加性基因-环境交互作用（additive gene-environment interaction），而不是广泛使用的乘法尺度（multiplicative scale），作为更相关的公共卫生措施。使用基因-环境独立性（gene-environment independence）可以提高病例对照研究中检测乘法交互作用的统计功效。然而，在偏离这种假设的情况下，估计值会出现实质性偏差，相应检验的Ⅰ型错误率也会增加。本文将先前针对乘法交互作用开发的经验贝叶斯（empirical Bayes，EB）方法扩展到加性尺度。该方法以数据自适应的方式在偏差和效率之间进行权衡，为交互作用产生的相对超额风险（relative excess risk due to interaction）推导出了一个 EB 估计量，并提出了相应的 Wald 检验，检验在回顾性似然框架下采用一般回归设置。我们研究了病例对照数据中基因-环境关联对检验结果的影响。我们的模拟研究表明，EB 方法以数据自适应的方式使用基因-环境独立性假设，并与标准的逻辑回归分析相比提供了更大的功效增益，与假设基因-环境独立性的分析相比，更好地控制了Ⅰ型错误率。我们用卵巢癌协会联盟（Ovarian Cancer Association Consortium）的数据说明了这些方法。
