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Regression calibration with instrumental variables for longitudinal models with interaction terms, and application to air pollution studies

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  • M. Strand
  • S. Sillau
  • G. K. Grunwald
  • N. Rabinovitch

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. © 2015 The Authors Environmetrics Published by John Wiley & Sons Ltd.

Suggested Citation

  • M. Strand & S. Sillau & G. K. Grunwald & N. Rabinovitch, 2015. "Regression calibration with instrumental variables for longitudinal models with interaction terms, and application to air pollution studies," Environmetrics, John Wiley & Sons, Ltd., vol. 26(6), pages 393-405, September.
  • Handle: RePEc:wly:envmet:v:26:y:2015:i:6:p:393-405
    DOI: 10.1002/env.2354
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    Cited by:

    1. Duncan Lee & Chris Robertson & Colin Ramsay & Kate Pyper, 2020. "Quantifying the impact of the modifiable areal unit problem when estimating the health effects of air pollution," Environmetrics, John Wiley & Sons, Ltd., vol. 31(8), December.

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