{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T03:34:16Z","timestamp":1763436856946,"version":"3.41.2"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T00:00:00Z","timestamp":1747008000000},"content-version":"vor","delay-in-days":11,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R21 AG063370","R21 AG068955","R01 AG081244","R01 AG069120","UL1 TR002345"],"award-info":[{"award-number":["R21 AG063370","R21 AG068955","R01 AG081244","R01 AG069120","UL1 TR002345"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Trans-Omics in Precision Medicine"},{"DOI":"10.13039\/100000050","name":"National Heart, Lung and Blood Institute","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Molecular Genomics Core"},{"name":"Keck School of Medicine of the University of Southern California","award":["HHSN268201600038I"],"award-info":[{"award-number":["HHSN268201600038I"]}]},{"name":"TOPMed Informatics Research Center","award":["3R01HL-117626-02S1","HHSN268201800002I"],"award-info":[{"award-number":["3R01HL-117626-02S1","HHSN268201800002I"]}]},{"name":"TOPMed Data Coordinating Center","award":["R01HL-120393","U01HL-120393","HHSN268201800001I"],"award-info":[{"award-number":["R01HL-120393","U01HL-120393","HHSN268201800001I"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Mediation analysis with high-dimensional mediators is crucial for identifying epigenetic pathways linking environmental exposures to health outcomes. However, high-dimensional mediation analysis methods for longitudinal mediators and a survival outcome remain underdeveloped. This study fills that gap by introducing a method that captures mediation effects over time using multivariate, longitudinally measured time-varying mediators. Our approach uses a longitudinal mixed effects model to examine the relationship between the exposure and the mediating process. We connect the mediating process to the survival outcome using a Cox proportional hazards model with time-varying mediators. To handle high-dimensional data, we first employ a mediation-based sure independence screening method for dimension reduction. A Lasso inference procedure is further utilized to identify significant time-varying mediators. We adopt a joint significance test to accurately control the family wise error rate in testing high-dimensional mediation hypotheses. Simulation studies and an analysis of the Coronary Artery Risk Development in Young Adults Study demonstrate the utility and validity of our method.<\/jats:p>","DOI":"10.1093\/bib\/bbaf206","type":"journal-article","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T04:34:08Z","timestamp":1747024448000},"source":"Crossref","is-referenced-by-count":1,"title":["High-dimensional mediation analysis for longitudinal mediators and survival outcomes"],"prefix":"10.1093","volume":"26","author":[{"given":"Lili","family":"Liu","sequence":"first","affiliation":[{"name":"Center for Biostatistics and Data Science , Washington University in St. Louis, 660 S. 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