Improving the Power to Detect Indirect Effects in Mediation Analysis

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 consider the principle of intersection–union tests and a method called the S-test. This method increases power but is not appropriate for statistical tests as small significance levels may cause the test to reject a null hypothesis, but larger significance levels will not reject the same hypothesis. We propose two new methods that provide increased power over existing methods while controlling type I error. We demonstrate through extensive simulation that the S-test and proposed methods control type I error and increase power over existing methods, and that while the proposed methods do not have the same problems, they provide similar power to the S-test. Finally, we provide an application to a large proteomic study.