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Abstract:
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Third-variables refer to the middle variables that are positioned in the pathway between an exposure and an outcome variable. Mediation analysis is a statistical approach to identify third-variables, and to estimate and test third-variable effects that explain the exposure -- outcome association. In this talk, we propose three methods for mediation analysis in Bayesian settings: (1) the function of coefficients method, (2) the product of partial differences method, and (3) the resampling method. The explicit benefit of the Bayesian mediation analysis is that the hierarchical relationships between the exposure variable and third-variables, and between third-variables and the outcome are naturally built into the Bayesian models. We performed sensitivity analysis to assess the impact of the choice of prior distributions in the three Bayesian inference methods. We found that the proposed methods are robust across a range of priors. Finally, we illustrate the proposed methods using real data from the MY-Health Study to explore racial/ethnic disparities in anxiety among cancer survivors.
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