A0241
Title: A general approach to modeling environmental mixtures with multivariate outcomes
Authors: Glen McGee - University of Waterloo (Canada) [presenting]
Joseph Antonelli - University of Florida (United States)
Abstract: An important goal of environmental health research is to assess the health risks posed by mixtures of multiple environmental exposures. In these analyses, flexible models like Bayesian kernel machine regression and multiple index models are appealing because they allow for arbitrary non-linear exposure-outcome relationships. However, this flexibility comes at the cost of low power, particularly when exposures are highly correlated and the health effects are weak, as is typical in environmental health studies. An adaptive index modeling strategy is proposed, that borrows strength across exposures and outcomes by exploiting similar mixture component weights and exposure-response relationships. In the special case of distributed lag models, in which exposures are measured repeatedly over time, co-clustering of lag profiles and exposure-response curves is jointly encouraged to more efficiently identify critical windows of vulnerability and characterize important exposure effects. The proposed approach is then extended to the multivariate index model setting where the true index structure is unknown, and variable importance measures are introduced to quantify component contributions to mixture effects. Using time series data from the National Morbidity, Mortality and Air Pollution Study, the proposed methods are demonstrated by jointly modeling three mortality outcomes and two cumulative air pollution measurements with a maximum lag of 14 days.
