Bayesian Distributed Lag Interaction Model for Multiple Modifiers

Oregon Convention Center 

Epidemiological evidence supports an association between maternal exposure to air pollution and birth and child health outcomes. Typically, such associations are estimated by regressing a scalar outcome on daily or weekly measures of exposure during pregnancy using a distributed lag model. However, these associations may be modified by area- or individual-level factors. We propose a Bayesian distributed lag interaction model that allows for a continuous index, a weighted average of multiple modifiers, to modify the association between repeated measures of exposure and an outcome. We estimate our model with a spline cross-basis in a Bayesian hierarchical model. Our model framework allows for simultaneous estimation of index weights and the exposure-time-response function. The index parameterization regularizes the model when modifiers are correlated. Through simulations, we showed that our model out-performs competing methods when there are multiple modifiers of unknown importance. We applied our proposed method to a Colorado birth cohort and estimated the association between birth weight and air pollution modified by a continuous index comprising area- and individual-level factors.

distributed lag models

bayesian hierarchical models

splines

effect modification

environmental epidemiology

fine particulate matter 

Section on Statistics in Epidemiology