|
|||||
|
|
Bayesian Inference for the Causal Effect of Mediation |
|
| Authors: | Michael J Daniels Jason A Roy Chanmin Kim Joseph W Hogan Michael G Perri |
| Institution: | 1. Department of Statistics, University of Florida, Gainesville, Florida 32611, U.S.A.;2. Department of Biostatistics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, U.S.A.;3. Department of Biostatistics, Brown University, Providence, Rhode Island 02912, U.S.A.;4. Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida 32611, U.S.A. |
| Abstract: | Summary We propose a nonparametric Bayesian approach to estimate the natural direct and indirect effects through a mediator in the setting of a continuous mediator and a binary response. Several conditional independence assumptions are introduced (with corresponding sensitivity parameters) to make these effects identifiable from the observed data. We suggest strategies for eliciting sensitivity parameters and conduct simulations to assess violations to the assumptions. This approach is used to assess mediation in a recent weight management clinical trial. |
| Keywords: | Causal inference Direct effect Indirect effect Mediators Nonparametric Bayes Sensitivity analysis |