The importance of mediation analysis in health and medical research relies on the need to illuminate the mechanisms by which an exposure exerts a causal effect on an outcome variable. In practice, a mediational model hypothesizes that the exposure variable causes one or more measured intermediate variables (mediators), which in turn affects the outcome variable. Traditionally, causal mediation analysis has been implemented within the framework of linear structural equation modeling (LSEM) involving a series of liner regression models and two approaches have been proposed to quantify mediation as difference in coefficients method and product of coefficients method. For regression models using standard least squares without missing data, the estimators from both approaches are identical. Recently, the methods developed in the LSEM framework were generalized to nonlinear models, e.g. proportional hazards model for survival data. Warner et al. and Lu et al. compared the hazard ratios of the exposure from Cox model both with and without adjusting for the potential mediators and a change in hazard ratios is taken as evidence of mediation through the mediators. Lange and Hansen argued that, such mediation analysis for survival data has severe shortcomings. First of all, the observed changes in hazard ratios cannot be given a causal interpretation, and also it cannot be satisfied that the proportional hazards assumption holds for both models with and without the mediator.
Author(s): Wei Wang