Abstract Details
Activity Number:
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653
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Type:
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Contributed
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Date/Time:
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Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #307524 |
Title:
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Bayesian Inference for Longitudinal Mediation Analysis
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Author(s):
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Chanmin Kim*+ and Michael Daniels and Jason Roy
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Companies:
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University of Florida and The University of Texas at Austin and University of Pennsylvania
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Keywords:
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Bayesian Inference ;
Causal effects ;
Longitudinal Mediation ;
Bayesian updating model
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Abstract:
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We propose a Bayesian approach to estimate the natural direct and indirect effects in the setting of longitudinal mediators and responses. A Bayesian updating model is incorporated to link the causal effects across the different time points. This procedure involves imputing missing data sequentially. Several conditional independence assumptions (with corresponding sensitivity parameters) are introduced to identify causal effects at each time. This approach is used to assess longitudinal mediation in the CTQ II clinical trial which contains a large number of intermittent missing values and dropouts.
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Authors who are presenting talks have a * after their name.
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