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Activity Number: 2
Type: Invited
Date/Time: Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract - #307121
Title: Bayesian Effect Estimation Accounting for Adjustment Uncertainty
Author(s): Giovanni Parmigiani*+ and Francesca Dominici and Chi Wang
Companies: Dana-Farber Cancer Institute and Harvard School of Public Health and University of Kentucky
Keywords: Adjustment uncertainty ; Bayesian model averaging ; Exposure effects ; Treatment effects

Model-based estimation of the effect of an exposure on an outcome is generally sensitive to the choice of which confounding factors are included in the model. IN a recent Biometrics article we propose a new approach, which we call Bayesian adjustment for confounding (BAC), to estimate the effect of an exposure of interest on the outcome, while accounting for the uncertainty in the choice of confounders. Our approach is based on specifying two models: (1) the outcome as a function of the exposure and the potential confounders (the outcome model); and (2) the exposure as a function of the potential confounders (the exposure model). We consider Bayesian variable selection on both models and link the two by introducing a dependence parameter, ?, denoting the prior odds of including a predictor in the outcome model, given that the same predictor is in the exposure model. In the absence of dependence (?= 1), BAC reduces to traditional Bayesian model averaging (BMA). This lecture will provide an overview of BAC, discuss its performance in controlled simulations, and briefly highlight some connections with causal inference methods.

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