{"doi":"10.1101/2025.05.23.655653","title":"Joint Modeling of Longitudinal Biomarker and Survival Outcomes with the Presence of Competing Risk in Nested Case-Control Studies with Application to the TEDDY Microbiome Dataset","abstract":"Motivation: Large-scale prospective cohort studies collect longitudinal biospecimens alongside time-to-event outcomes to investigate biomarker dynamics in relation to disease risk. The nested case-control (NCC) design provides a cost-effective alternative to full cohort biomarker studies while preserving statistical efficiency. Despite advances in joint modeling for longitudinal and time-to-event outcomes, few approaches address the unique challenges posed by NCC sampling, non-normally distributed biomarkers, and competing survival outcomes. Results: Motivated by the TEDDY study, we propose \"JM-NCC\", a joint modeling framework designed for NCC studies with competing events. It integrates a generalized linear mixed-effects model for potentially non-normally distributed biomarkers with a cause-specific hazard model for competing risks. Two estimation methods are developed. fJM-NCC leverages NCC sub-cohort longitudinal biomarker data and full cohort survival and clinical metadata, while wJM-NCC uses only NCC sub-cohort data. Both simulation studies and an application to TEDDY microbiome dataset demonstrate the robustness and efficiency of the proposed methods.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":566925,"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":0.951,"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":834769,"name":"TingFang Lee","orcid":null,"position":1,"is_corresponding":false},{"id":651928,"name":"Boyan Zhou","orcid":"0000-0001-7067-4578","position":2,"is_corresponding":false},{"id":382731,"name":"Chan Wang","orcid":"0000-0002-1718-5414","position":3,"is_corresponding":false},{"id":244582,"name":"Ann Marie Schmidt","orcid":"0000-0001-8902-070X","position":4,"is_corresponding":false},{"id":329106,"name":"Mengling Liu","orcid":"0000-0001-9758-8522","position":5,"is_corresponding":false},{"id":315159,"name":"Huilin Li","orcid":"0000-0002-8288-7068","position":6,"is_corresponding":false},{"id":246046,"name":"Jiyuan Hu","orcid":"0000-0003-1332-5587","position":7,"is_corresponding":false},{"id":331942,"name":"Yanan Zhao","orcid":"0000-0002-2664-2795","position":0,"is_corresponding":true}],"reference_count":55,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:56:40.491968Z","pmid":"40501688","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":[]}