Mediation analysis with multiple exposures, mediators, and outcomes

Oregon Convention Center 

Mediation analysis with one exposure (X) and one outcome (Y) is fairly well
developed. When there are multiple exposures, mediators, and outcomes, there
appear to be no standard concepts or formulas.
Also, if there are multiple exposures or mediators, the effect of one exposure
on an outcome may be partially mediated by the correlation of that exposure
with other exposures; the same is true of outcomes. This means that there may
be an effect of an exposure (X) or mediator (Z) on an outcome (Y) although a
linear model for Y may indicate that X or Z is not significant as a direct
covariate.
We have developed measures of direct and indirect effects of exposures on
outcomes in the situation where there are multiple exposures, mediators, and
outcomes, and all models are linear. The measures are derived from path
analysis as developed by Wright. We demonstrate with an example based on
simulated data.

Mediation analysis

multiple covariates

path analysis

sparse data 

Biometrics Section