Quantile causal mediation analysis allowing longitudinal data.

ABSTRACT: Mediation analysis has mostly been conducted with mean regression models. With this approach modeling means, formulae for direct and indirect effects are based on changes in means, which may not capture effects that occur in units at the tails of mediator and outcome distributions. Individuals with extreme values of medical endpoints are often more susceptible to disease and can be missed if one investigates mean changes only. We derive the controlled direct and indirect effects of an exposure along percentiles of the mediator and outcome using quantile regression models and a causal framework. The quantile regression models can accommodate an exposure-mediator interaction and random intercepts to allow for longitudinal mediator and outcome. Because DNA methylation acts as a complex "switc

SUBMITTER: Bind MA 

PROVIDER: S-EPMC5788575 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature