Measurement errors in Gaussian mixture models using high-dimensional air pollution constituents data

Music City Center 

Assessing the impact of air pollution components on health outcomes is a crucial aspect of air pollution epidemiology.
The statistical analysis of air pollution data encounters bias due to measurement errors of exposures to air pollution.
It is well established that exposure to fine particles (PM2.5) can cause a variety of adverse health outcomes. However, the specific impacts of PM2.5 constituents are less studied.
We proposed a novel approach to correct the bias resulting from measurement errors associated with multiple correlated air pollutants and a complex correlated error structure.
Through the framework of main and external validation designs,
our proposed method provides a general structure of joint model for data generation. This model integrates a Gaussian mixture model, a measurement error model and a outcome model.
We employ the Expectation-maximization algorithm to simultaneously estimate all the parameters in the joint model.
The application of this method is illustrated in a study investigating the association between air pollution exposures and cognitive function within the Nurses' Health Study (NHS).

measurement errors

Gaussian mixture model

air pollution

Bayesian joint modeling

main and validation studies 

Section on Statistics in Epidemiology