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Numerous studies have examined the associations between long-term exposure tofine particulate matter (PM2.5) and adverse health outcomes. Recently, many ofthese studies have begun to employ high-resolution predicted PM2.5concentrations, which are subject to measurement error. Previous approaches forexposure measurement error correction have either been applied in non-causalsettings or have only considered a categorical exposure. Moreover, mostprocedures have failed to account for uncertainty induced by error correctionwhen fitting an exposure-response function (ERF). To remedy these deficiencies,we develop a multiple imputation framework that combines regression calibrationand Bayesian techniques to estimate a causal ERF. We demonstrate how the outputof the measurement error correction steps can be seamlessly integrated into aBayesian additive regression trees (BART) estimator of the causal ERF. We alsodemonstrate how locally-weighted smoothing of the posterior samples from BARTcan be used to create a more accurate ERF estimate. Our proposed approach alsoproperly propagates the exposure measurement error uncertainty to yieldaccurate standard error estimates. We assess the robustness of our proposedapproach in an extensive simulation study. We then apply our methodology toestimate the effects of PM2.5 on all-cause mortality among Medicare enrolleesin New England from 2000-2012.

Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality