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Bayesian Inference of Chemical Mixtures Exposures in Risk Assessment of Health Outcome

Abstract Number:

3838 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Debamita Kundu (1), Sung Duk Kim (2), Paul Albert (2)

Institutions:

(1) N/A, N/A, (2) National Cancer Institute, N/A

Co-Author(s):

Sung Duk Kim  
National Cancer Institute
Paul Albert  
National Cancer Institute

First Author:

Debamita Kundu  
N/A

Presenting Author:

Debamita Kundu  
N/A

Abstract Text:

Analyzing health effects associated with exposure to environmental chemical mixtures is a challenging problem in epidemiology, toxicology, and exposure science. In particular, when there are a large number of chemicals under consideration it is difficult to estimate the interactive effects without incorporating reasonable prior information. Based on substantive considerations, researchers believe that true interactions between chemicals need to incorporate their corresponding main effects. In this paper, we use this prior knowledge through a shrinkage prior that a priori assumes an interaction term can only occur when the corresponding main effects exist. Our initial development is for logistic regression with linear chemical effects. We extend this formulation to include non-linear exposure effects and to account for exposure subject to detection limit. We develop an MCMC algorithm using a shrinkage prior that shrinks the interaction terms closer to zero as the main effects get closer to zero. We examine the performance of our methodology through simulation studies and illustrate an analysis of chemical interactions in a case-control study in cancer.

Keywords:

Chemical mixture|Interaction|Shrinkage|Collapsed Gibbs| |

Sponsors:

Section on Statistics in Epidemiology

Tracks:

Statistical Issues in Environmental Epidemiology

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