Sensitivity analyses for parametric causal mediation effect estimation.

ABSTRACT: Causal mediation analysis uses a potential outcomes framework to estimate the direct effect of an exposure on an outcome and its indirect effect through an intermediate variable (or mediator). Causal interpretations of these effects typically rely on sequential ignorability. Because this assumption is not empirically testable, it is important to conduct sensitivity analyses. Sensitivity analyses so far offered for this situation have either focused on the case where the outcome follows a linear model or involve nonparametric or semiparametric models. We propose alternative approaches that are suitable for responses following generalized linear models. The first approach uses a Gaussian copula model involving latent versions of the mediator and the final outcome. The second approach uses a

SUBMITTER: Albert JM 

PROVIDER: S-EPMC4441101 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature