Accounting for space and time in measurement error corrections

Abstract

Measurement error is endemic in epidemiology and arguments over the direction of bias persist. Modern high-accuracy models of ambient concentrations of air pollutants have reduce error. We illustrate the use of machine learning algorithms to estimate the spatial and temporal variation of exposure error in simulation studies to estimate the direction and extent of bias in air pollution epidemiology using linear, nonlinear, and threshold relationships. We also illustrate the use of regression calibration models that account for differences in calibration across space. We also discuss the parallel between using ambient instead of personal exposure and intention to treat analyses.