A scalable two-stage Bayesian approach accounting for exposure measurement error in epidemiology
Abstract Number:
1976
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Changwoo Lee (1), Elaine Symanski (2), Amal Rammah (2), Dong Hun Kang (3), Philip Hopke (4), Eun Sug Park (3)
Institutions:
(1) Texas A&M University, N/A, (2) Baylor College of Medicine, N/A, (3) Texas A&M Transportation Institute, N/A, (4) University of Rochester School of Medicine and Dentistry, N/A
Co-Author(s):
Philip Hopke
University of Rochester School of Medicine and Dentistry
First Author:
Presenting Author:
Abstract Text:
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.
Keywords:
Environmental health|Spatial exposure measurement error
|Two-stage Bayesian model|Uncertainty propagation|Vecchia approximation|
Sponsors:
Section on Statistics in Epidemiology
Tracks:
Statistical Issues in Environmental Epidemiology
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