Variance component tests of multivariate mediation effects under composite null hypotheses

Yen‐Tsung Huang

Biometrics, 2019, vol. 75, issue 4, 1191-1204

Abstract: Mediation effects of multiple mediators are determined by two associations: one between an exposure and mediators ( S‐ M) and the other between the mediators and an outcome conditional on the exposure ( M‐ Y). The test for mediation effects is conducted under a composite null hypothesis, that is, either one of the S‐ M and M‐ Y associations is zero or both are zeros. Without accounting for the composite null, the type 1 error rate within a study containing a large number of multimediator tests may be much less than the expected. We propose a novel test to address the issue. For each mediation test j, j = 1 , … , J, we examine the S‐ M and M‐ Y associations using two separate variance component tests. Assuming a zero‐mean working distribution with a common variance for the element‐wise S‐ M (and M‐ Y) associations, score tests for the variance components are constructed. We transform the test statistics into two normally distributed statistics under the null. Using a recently developed result, we conduct J hypothesis tests accounting for the composite null hypothesis by adjusting for the variances of the normally distributed statistics for the S‐ M and M‐ Y associations. Advantages of the proposed test over other methods are illustrated in simulation studies and a data application where we analyze lung cancer data from The Cancer Genome Atlas to investigate the smoking effect on gene expression through DNA methylation in 15 114 genes.

Date: 2019
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