{"doi":"10.1093/bib/bbaf206","title":"High-dimensional mediation analysis for longitudinal mediators and survival outcomes","abstract":"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.","journal":"Briefings in Bioinformatics","year":2025,"id":565712,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9523,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":667925,"name":"Haixiang Zhang","orcid":"0000-0002-7311-5605","position":1,"is_corresponding":false},{"id":291153,"name":"Yinan Zheng","orcid":"0000-0002-2006-7320","position":2,"is_corresponding":false},{"id":666562,"name":"Tao Gao","orcid":"0000-0002-9462-9349","position":3,"is_corresponding":false},{"id":625721,"name":"Cheng Zheng","orcid":"0000-0002-6562-870X","position":4,"is_corresponding":false},{"id":1469311,"name":"Kai Zhang","orcid":"0000-0002-2747-3962","position":5,"is_corresponding":false},{"id":218546,"name":"Lifang Hou","orcid":"0000-0003-4877-0031","position":6,"is_corresponding":false},{"id":667926,"name":"Lei Liu","orcid":"0000-0003-1844-338X","position":7,"is_corresponding":false},{"id":1357626,"name":"Lili Liu","orcid":"0009-0003-5594-3022","position":0,"is_corresponding":true}],"reference_count":45,"raw_metadata":null,"created_at":"2026-07-19T02:56:32.546082Z","pmid":"40350699","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}