A Flexible Bayesian Quantile G-Computation Approach for Modeling Environmental Mixtures Over Space

Music City Center 

Methods for environmental mixtures ("mixtures analyses") consider the overall effect of multiple concurrent exposures instead of solely one exposure at a time. Mixtures analyses are particularly important for environmental exposures as overall effects can differ from effects of any single exposure alone. Crucially, these analyses are often conducted in small samples and mixtures analyses generally do not account for the potential spatial dependence of health outcomes, which may lead to poor characterization of overall effect estimates. To address this, we develop a novel flexible Bayesian quantile g-computation (BQGC) approach to mixtures. This methodologic innovation not only incorporates spatial dependence, but also leverages known groupings of exposures (e.g. grouping metals as essential or non-essential) through hierarchical modeling. Using simulations, we compare our approach to other mixtures methods, such as quantile g-computation and (Bayesian) weighted quantile sums regression. We compare bias, root mean square error (RMSE), and 95% coverage probability across approaches. Relative to mixtures methods that did not address spatial dependence, RMSE was improved in BQGC when strong spatial correlation was present, though precision benefits came at the cost of bias (and therefore under-coverage) in spatial models. We demonstrate our approach using the New England Bladder Cancer Study (NEBCS), a large, population-based case-control study. We estimate associations for a mixture of heavy metals and bladder cancer incidence in NEBCS. Our innovations improve the accuracy of statistical evidence of the mixture's effect on disease risk and expand the flexibility of mixtures analysis in public health.

spatial statistics

spatial epidemiology

environmental epidemiology

mixtures

cancer