Faculty, Staff and Student Publications
Language
English
Publication Date
12-31-2025
Journal
Biostatistics
DOI
10.1093/biostatistics/kxae037
PMID
39412139
PMCID
PMC11823199
PubMedCentral® Posted Date
10-16-2024
PubMedCentral® Full Text Version
Post-print
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 to exclude the non-mediators, followed by ordinary least squares regressions for the variance estimation. We then constructed confidence intervals based on the newly derived closed-form asymptotic distribution of the R2 measure. Extensive simulation studies demonstrated that this proposed procedure is much more computationally efficient than the resampling-based method, with comparable coverage probability. Furthermore, when applied to the Framingham Heart Study, the proposed method replicated the established finding of gene expression mediating age-related variation in systolic blood pressure and identified the role of gene expression profiles in the relationship between sex and high-density lipoprotein cholesterol level. The proposed estimation procedure is implemented in R package CFR2M.
Keywords
Humans, Mediation Analysis, Models, Statistical, Male, Computer Simulation, Female, Blood Pressure, Data Interpretation, Statistical, confidence interval, cross-fitting, gene expression, iterative sure independence screening, mediation analysis, R 2 total mediation effect measure
Published Open-Access
yes
Recommended Citation
Xu, Zhichao; Li, Chunlin; Chi, Sunyi; et al., "Speeding Up Interval Estimation for R2-Based Mediation Effect of High-Dimensional Mediators via Cross-Fitting" (2025). Faculty, Staff and Student Publications. 6778.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6778
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Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons