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Activity Number: 606
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #320964 View Presentation
Title: Bayesian Model Averaging Applied to Tuberculosis and HIV Research Studies
Author(s): Brock Stewart* and Charles E. Rose and Yi Pan
Companies: CDC and CDC and CDC
Keywords: Bayesian Model Averaging ; Model Building ; Tuberculosis ; HIV

Bayesian Model Averaging (BMA) has emerged as a useful model-building methodology over the past several decades. It may be especially useful to researchers who are faced with a large number of potential predictor variables of unknown utility in answering the study questions at hand. One benefit of BMA is that it does not require the analyst to apriori choose a subset of predictor variables, but rather allows for each variable to be weighted according to its posterior probability of being included in the model. We explore the features of BMA on two real public health datasets are aimed at studying new therapies for treating patients with latent Tuberculosis infection and HIV, respectively.

Authors who are presenting talks have a * after their name.

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