  Causal mediation analysis seeks to determine whether an independent variable
affects a response variable directly or whether it does so indirectly, by way
of a mediator. The existing statistical tests to determine the existence of an
indirect effect are overly conservative or have inflated type I error. In this
article, we propose two methods based on the principle of intersection-union
tests that offer improvements in power while controlling the type I error. We
demonstrate the advantages of the proposed methods through extensive
simulation. Finally, we provide an application to a large proteomic study.
