2019/10/28 by Ruixuan Zhou, Zhou, Ruixuan Rachel, Liewei Wang +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Mathematics · #FOS: Computer and information sciences #Gene expression and cancer classification #Methodology (stat.ME) #Statistical Methods and Inference #Statistical Methods in Clinical Trials
paper · pdf · doi:10.48550/arxiv.1910.12457
openalex publication_date 2019/10/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Mediation analysis is difficult when the number of potential mediators is\nlarger than the sample size. In this paper we propose new inference procedures\nfor the indirect effect in the presence of high-dimensional mediators for\nlinear mediation models. We develop methods for both incomplete mediation,\nwhere a direct effect may exist, as well as complete mediation, where the\ndirect effect is known to be absent. We prove consistency and asymptotic\nnormality of our indirect effect estimators. Under complete mediation, where\nthe indirect effect is equivalent to the total effect, we further prove that\nour approach gives a more powerful test compared to directly testing for the\ntotal effect. We confirm our theoretical results in simulations, as well as in\nan integrative analysis of gene expression and genotype data from a\npharmacogenomic study of drug response. We present a novel analysis of gene\nsets to understand the molecular mechanisms of drug response, and also identify\na genome-wide significant noncoding genetic variant that cannot be detected\nusing standard analysis methods.\n