Speeding up interval estimation for R2-based mediation effect of high-dimensional mediators via cross-fitting.

ABSTRACT: Mediation analysis is a useful tool in investigating how molecular phenotypes such as gene expression mediate the effect of exposure on health outcomes. However, commonly used mean-based total mediation effect measures may suffer from cancellation of component-wise mediation effects in opposite directions in the presence of high-dimensional omics mediators. To overcome this limitation, we recently proposed a variance-based R-squared total mediation effect measure that relies on the computationally intensive nonparametric bootstrap for confidence interval estimation. In the work described herein, we formulated a more efficient two-stage, cross-fitted estimation procedure for the R2 measure. To avoid potential bias, we performed iterative Sure Independence Screening (iSIS) in two subsamples

SUBMITTER: Xu Z 

PROVIDER: S-EPMC11823199 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature