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Activity Number: 653
Type: Contributed
Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #307524
Title: Bayesian Inference for Longitudinal Mediation Analysis
Author(s): Chanmin Kim*+ and Michael Daniels and Jason Roy
Companies: University of Florida and The University of Texas at Austin and University of Pennsylvania
Keywords: Bayesian Inference ; Causal effects ; Longitudinal Mediation ; Bayesian updating model
Abstract:

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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