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Activity Number: 520
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #305629
Title: A Robust Bayesian Approach to Assessing Average Indirect (Mediation) Effect in Multilevel Models, with Application to a Smoking Study
Author(s): Yisheng Li*+ and Ying Yuan
Companies: MD Anderson Cancer Center and MD Anderson Cancer Center
Address: Department of Biostatistics, Houston, TX, 77030, United States
Keywords: mediation analysis ; indirect effect ; multilevel model ; mixed-effects model ; Dirichlet process ; nonparametric Bayes

Mediation analysis is commonly conducted in the behavioral and social sciences with a goal of assessing whether the effect of an independent variable X on an outcome variable Y is through an intermediate variable M. Two regression models are fit, one being M on X (path a) and the other being Y on both X and M (path b). When X, Y and M all involve repeated measurements within clusters, paths a and b are typically multilevel (mixed-effects) models in which the effects of X on M and M on Y controlling for X are assumed cluster-specific and normally distributed. The normality assumption may result in poor performance when the random effects are not normally distributed (Bauer et al, 2006). We propose a Bayesian nonparametric approach to testing the average indirect effect using a Dirichlet process prior for the distribution of the random effects in both paths a and b. This approach makes use of recent results on the calculation of the moments of the random moments of the nonparametric random effect distribution with a Dirichlet process prior (Li et al., 2011). We evaluate the performance of the proposed approach using simulations, and illustrate the method with a smoking dataset.

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