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