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

TL;DR

This paper introduces a novel Bayesian multiple imputation framework combining regression calibration and Bayesian additive regression trees to accurately estimate causal exposure-response functions in the presence of measurement error, applied to PM2.5 and mortality data.

Contribution

The paper develops a new methodology that accounts for measurement error and uncertainty in estimating causal exposure-response functions using Bayesian techniques and BART.

Findings

The proposed method effectively corrects for measurement error in simulated data.

Application to Medicare data reveals significant associations between PM2.5 and mortality.

The approach provides accurate standard errors by propagating measurement error uncertainty.

Abstract

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