A scalable two-stage Bayesian approach accounting for exposure measurement error in epidemiology

Oregon Convention Center 

Accounting for exposure measurement errors has been recognized as a crucial problem in environmental epidemiology. Bayesian hierarchical models offer a coherent probabilistic framework for evaluating associations between environmental exposures and health effects, which take into account exposure measurement errors introduced by uncertainty in exposure estimates as well as spatial misalignment. While 2-stage Bayesian analyses are often regarded as a good alternative to fully Bayesian analyses when joint estimation is not feasible, there has been minimal research on how to properly propagate uncertainty from the exposure model to the health model in the case of a large number of participant locations along with spatially correlated exposures. We propose a scalable 2-stage Bayesian approach, called a sparse MVN prior approach, based on Vecchia approximation. We compare its performance with existing approaches via simulation, demonstrating results comparable to the fully Bayesian approach. We investigate the association between source-specific and pollutant-specific exposures and birth outcomes for 2012 in Harris County, Texas, using several approaches, including the proposed method.

Environmental health

Spatial exposure measurement error


Two-stage Bayesian model

Uncertainty propagation

Vecchia approximation 

Section on Statistics in Epidemiology