title: Regression calibration with instrumental variables for longitudinal models with interaction terms, and application to air pollution studies.
abstract: 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 (LTE4, 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.
authors.affiliation: 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.
authors.forename: M
authors.initials: M
authors.lastname: Strand
authors.affiliation: Department of Neurology, Colorado School of Medicine, University of Colorado Denver Denver, CO, U.S.A.
authors.forename: S
authors.initials: S
authors.lastname: Sillau
authors.affiliation: Department of Biostatistics & Informatics, Colorado School of Public Health, University of Colorado Denver Denver, CO, U.S.A.
authors.forename: G K
authors.initials: GK
authors.lastname: Grunwald
authors.affiliation: Department of Pediatrics, National Jewish Health Denver, CO, U.S.A.
authors.forename: N
authors.initials: N
authors.lastname: Rabinovitch
keywords: LTE4; ambient PM2.5; cigarette smoke; cotinine; errors in variables; measurement error