Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth
Understanding the impact that ambient air pollution exposure during different stages of pregnancy has on the risk of adverse birth outcomes is vital for protection of the fetus and for the development of mechanistic explanations for disease evolution. As a result, statistical models that seek to estimate critical windows of susceptibility have been developed and applied to a number of different outcomes and pollutants. However, these current methods fail to address the primary objective of this line of research; how to statistically identify a critical window of susceptibility?
In this work, we introduce critical window variable selection (CWVS), a hierarchical Bayesian framework which directly addresses this issue while simultaneously providing improved estimation of the risk parameters of interest. Through simulation, we show that CWVS outperforms the standard Gaussian process and stochastic search variable selection techniques in the setting of highly temporally correlated exposures in terms of (i) correctly identifying critical windows and (ii) accurately estimating the risk parameters. We apply all competing methods to a case/control analysis of pregnant women in North Carolina, 2005-2008, with respect to the development of very preterm birth and exposure to ambient ozone and particulate matter less than or equal to 2.5 micrometers in aerodynamic diameter, and identify/estimate the critical windows of susceptibility.