Title: Bayesian joint modeling for causal mediation analysis with a binary outcome and a binary mediator
Authors: Miguel Caubet Fernandez - Universite du Quebec a Montreal (Canada)
Mariia Samoilenko - Universite du Quebec a Montreal (Canada)
Genevieve Lefebvre - Universite du Quebec a Montreal (Canada) [presenting]
Abstract: Mediation analysis with a binary outcome is notoriously more challenging than with a continuous outcome. We will present a new approach for performing causal mediation with a binary outcome and a binary mediator. Our proposal relies on the Student-$t$ approximation to the Bayesian multivariate regression logistic model. We will explain how this latent multivariate model can be used to estimate the natural direct and indirect effects of an exposure on an outcome in any measuring scale of interest (e.g., odds or risk ratio, risk difference). The novel mediation approach has several valuable features which, to our knowledge, are not found together in current binary-binary mediation approaches. The model will be illustrated and compared to two existing approaches for conducting causal mediation analyses with this type of data.