A Bayesian Approach to the Causal Effect of Multiple Mediators with Sensitivity Analysis

• Activity Number: 115
• Type: Topic Contributed
• Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Chanmin Kim*+ and Michael Daniels and Joseph Hogan
• Companies: University of Texas at Austin and University of Texas at Austin and Brown University
• Keywords: Multiple mediators ; Joint indirect effect ; Individual indirect effect

Abstract

We propose a Bayesian approach to estimate the natural direct and the joint indirect effect through multiple mediators in the setting of continuous mediators and a binary response. We can decompose the joint indirect effect into each individual indirect effect while preserving other effects, in particular the total effect. Instead of assuming sequential ignorability, several conditional independence assumptions are introduced (with corresponding sensitivity parameters) to identify unidentifiable distributions with nonparametric modeling strategies. We suggest strategies for specifying sensitivity parameters and illustrate our approach to assess mediation in a physical activity promotion trial.