Joint Modeling of Longitudinal Biomarker and Survival Outcomes with the Presence of Competing Risk in the Nested Case-Control Studies with Application to the TEDDY Microbiome Dataset.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41570114", "kind": "science-record", "revision": 3, "content_hash": "sha256:c5dbd6bf38e84ae6894f4b77422eb4cadf0fb1fc7d9d0f51b277661d90d45410", "created_at": "2026-11-01T20:31:32Z", "updated_at": "2026-11-01T22:15:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Joint Modeling of Longitudinal Biomarker and Survival Outcomes with the Presence of Competing Risk in the Nested Case-Control Studies with Application to the TEDDY Microbiome Dataset.", "doi": "10.1093/bioinformatics/btag038", "pmid": "41570114", "authors": ["Yanan Zhao", "Ting-Fang Lee", "Boyan Zhou", "Chan Wang", "Ann Marie Schmidt", "Mengling Liu", "Huilin Li", "Jiyuan Hu"], "journal": "Bioinformatics (Oxford, England)", "year": 2026, "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.\nRESULTS: 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.\nAVAILABILITY: Software is available at https://github.com/Zhaoyn-oss/JMNCC and archived on Zenodo at https://zenodo.org/records/18199759 (DOI: 10.5281/zenodo.18199759).\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-method", "statement": "We present two MLE approaches for estimating ϕ, referred to as fJM-NCC and wJM-NCC respectively.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41570114", "locator": "sections[7] \"2.1.4 Survival sub-model\" paragraph 6", "quote": "We present two MLE approaches for estimating ϕ, referred to as fJM-NCC and wJM-NCC respectively."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The TEDDY microbiome 16S rRNA gene sequencing data and shotgun whole-genome sequencing data are publicly available in the NCBI database of Genotypes and Phenotypes (dbGaP) with the primary accession code phs001443.v1.p1, in accordance with the dbGaP controlled-access authorization process. 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