Exploiting gene-environment independence for analysis of case-control studies: An empirical bayes-type shrinkage estimator to trade-off between bias and efficiency
Exploiting gene-environment independence for analysis of case-control studies: An empirical bayes-type shrinkage estimator to trade-off between bias and efficiency
复制标题
DOI：
10.1111/j.1541-0420.2007.00953.x
复制
发表时间：
2008-09-01
期刊：
影响因子：
1.9
通讯作者：
Chatterjee, Nilanjan
中科院分区：
文献类型：
--
作者：
Mukherjee, Bhramar;Chatterjee, Nilanjan
病例对照数据的标准前瞻性Logistic回归分析往往导致对基因-环境相互作用的非常不准确的估计，这是因为在交叉基因型和暴露的细胞中的病例或对照数量很少。相反，在基因-环境独立性的假设下，现代的“回溯”方法，包括“仅病例”方法，可以更准确地估计相互作用参数，但当基因-环境独立性的基本假设被违反时，它们可能存在严重的偏差。在这篇文章中，我们提出了一种新的经验贝叶斯型收缩估计来分析病例对照数据，它可以以数据自适应的方式放松基因-环境独立性的假设。在涉及二元基因和二元暴露的特殊情况下，该方法导致相互作用对数优势比参数的简单闭合形式的估计，该估计对应于标准仅病例和病例对照估计的加权平均。我们还描述了在Chatterjee和Carroll(2005，Bitoiska 92,399-418)开发的回溯性最大似然框架内导出新的收缩估计器及其方差的一般方法。模拟和真实数据例子都表明，所提出的估计器在偏差和效率之间取得了平衡，这取决于基因-环境关联的真实性质和给定研究的样本大小。
Standard prospective logistic regression analysis of case-control data often leads to very imprecise estimates of gene-environment interactions due to small numbers of cases or controls in cells of crossing genotype and exposure. In contrast, under the assumption of gene-environment independence, modern "retrospective" methods, including the "case-only" approach, can estimate the interaction parameters much more precisely, but they can be seriously biased when the underlying assumption of gene-environment independence is violated. In this article, we propose a novel empirical Bayes-type shrinkage estimator to analyze case-control data that can relax the gene-environment independence assumption in a data-adaptive fashion. In the special case, involving a binary gene and a binary exposure, the method leads to an estimator of the interaction log odds ratio parameter in a simple closed form that corresponds to an weighted average of the standard case-only and case-control estimators. We also describe a general approach for deriving the new shrinkage estimator and its variance within the retrospective maximum-likelihood framework developed by Chatterjee and Carroll (2005, Biometrika 92, 399-418). Both simulated and real data examples suggest that the proposed estimator strikes a balance between bias and efficiency depending on the true nature of the gene-environment association and the sample size for a given study.