Multiply robust inference for statistical interactions.
Multiply robust inference for statistical interactions.
复制标题
将统计相互作用的强大推断倍增。
DOI：
10.1198/016214508000001084
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发表时间：
2008-12-01
影响因子：
3.7
通讯作者：
Robins JM
中科院分区：
文献类型：
--
作者：
Vansteelandt S;Vanderweele TJ;Robins JM
越来越多的科学研究的主要焦点是确定两种暴露是否在它们对感兴趣的结果产生的影响中相互作用。相互作用通常通过拟合回归模型进行评估，其中线性预测因子包括这些暴露之间的乘积。当主要兴趣在于相互作用时，这种方法并不完全令人满意，因为当主要暴露效应或结果与外部因素之间的关联被错误指定时，它容易产生（可能严重）偏倚。因此，在这篇文章中，我们考虑条件均值模型与身份或日志链接，假设统计相互作用的一个有限维参数，但在其他方面未指定。我们发现，估计的相互作用参数往往是不可行的，在这个模型中，因为它需要非参数估计的辅助条件期望给定的高维变量。因此，我们认为“乘稳健估计”的工会模型，假设至少有一个工作的子模型举行。我们的方法是新颖的，它利用信息的联合分布的曝光条件的外部因素，在相互作用参数的兴趣作出推断。在随机试验或以家族为基础的遗传研究的特殊情况下，其中联合暴露分布是已知的设计或孟德尔遗传，由此产生的多重稳健的程序导致无交互作用的零假设的渐近分布检验的加性规模。我们通过模拟和随机随访研究的分析说明了方法。
A primary focus of an increasing number of scientific studies is to determine whether two exposures interact in the effect that they produce on an outcome of interest. Interaction is commonly assessed by fitting regression models in which the linear predictor includes the product between those exposures. When the main interest lies in the interaction, this approach is not entirely satisfactory because it is prone to (possibly severe) bias when the main exposure effects or the association between outcome and extraneous factors are misspecified. In this article, we therefore consider conditional mean models with identity or log link which postulate the statistical interaction in terms of a finite-dimensional parameter, but which are otherwise unspecified. We show that estimation of the interaction parameter is often not feasible in this model because it would require nonparametric estimation of auxiliary conditional expectations given high-dimensional variables. We thus consider ‘multiply robust estimation’ under a union model that assumes at least one of several working submodels holds. Our approach is novel in that it makes use of information on the joint distribution of the exposures conditional on the extraneous factors in making inferences about the interaction parameter of interest. In the special case of a randomized trial or a family-based genetic study in which the joint exposure distribution is known by design or by Mendelian inheritance, the resulting multiply robust procedure leads to asymptotically distribution-free tests of the null hypothesis of no interaction on an additive scale. We illustrate the methods via simulation and the analysis of a randomized follow-up study.
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