Strand M, Sillau S, Grunwald G K, Rabinovitch N
Division of Biostatistics & Bioinformatics, National Jewish Health Denver, CO, U.S.A. ; Department of Biostatistics & Informatics, Colorado School of Public Health, University of Colorado Denver Denver, CO, U.S.A.
Department of Neurology, Colorado School of Medicine, University of Colorado Denver Denver, CO, U.S.A.
Environmetrics. 2015 Sep;26(6):393-405. doi: 10.1002/env.2354. Epub 2015 Aug 10.
In this paper, we derive forms of estimators and associated variances for regression calibration with instrumental variables in longitudinal models that include interaction terms between two unobservable predictors and interactions between these predictors and covariates not measured with error; the inclusion of the latter interactions generalize results we previously reported. The methods are applied to air pollution and health data collected on children with asthma. The new methods allow for the examination of how the relationship between health outcome leukotriene E4 (LTE, a biomarker of inflammation) and two unobservable pollutant exposures and their interaction are modified by the presence or absence of upper respiratory infections. The pollutant variables include secondhand smoke and ambient (outdoor) fine particulate matter. Simulations verify the accuracy of the proposed methods under various conditions.
在本文中，我们推导了纵向模型中使用工具变量进行回归校准的估计量形式及相关方差，这些纵向模型包含两个不可观测预测变量之间的交互项，以及这些预测变量与无测量误差的协变量之间的交互项；纳入后者的交互项推广了我们之前报告的结果。这些方法应用于收集到的哮喘儿童的空气污染与健康数据。新方法能够检验健康结局白三烯E4（LTE，一种炎症生物标志物）与两种不可观测污染物暴露及其交互作用之间的关系是如何因上呼吸道感染的有无而改变的。污染物变量包括二手烟和环境（室外）细颗粒物。模拟验证了所提方法在各种条件下的准确性。
