Empirical measurement error bias correction for the effects of functions of the exposure history: cumulative average PM2.5 in relation to all-cause mortality

Abstract

Cohort studies in air pollution epidemiology construct the exposure metric as a cumulative average over a pre-determined time window. We present valid methods for empirically estimating the time window, and simultaneously estimating the exposure effect from this complex variable that is a function of multiple exposure measurements over time, correcting for exposure measurement error bias using validation data. Air pollution validation studies will be briefly presented. Then, methods will be illustrated in the Nurses’ Health Study’s investigation of PM2.5 on all-cause mortality. Valid confidence interval construction will be considered, and publicly available software for conducting these analyses will be shown.