{"paper":{"title":"Creating treatment and component hierarchies in component network meta-analysis","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"A workflow identifies uniquely estimable relative effects to build valid treatment hierarchies in component network meta-analysis.","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Adriani Nikolakopoulou, Audrey B\\'eliveau, Augustine Wigle, Lifeng Lin","submitted_at":"2026-05-14T17:46:51Z","abstract_excerpt":"Component network meta-analysis (CNMA) is a statistical methodology that enables estimation of relative effects for multi-component treatments, such as combinations of antidepressants, and individual components, such as single antidepressants, by synthesizing data from multiple studies. A commonly desired output of a systematic review and meta-analysis is a hierarchy of the treatments in terms of a certain performance metric. Methods have been established for standard network meta-analysis (NMA), but have not yet been extended to CNMA. In particular, CNMA presents unique challenges because the"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We present a step-by-step workflow for answering treatment hierarchy questions in both frequentist and Bayesian CNMA, including explicitly identifying the uniquely estimable relative effects.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That identifying uniquely estimable relative effects is always feasible from the network structure and sufficient to construct valid non-misleading hierarchies without further assumptions on connectivity or data.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A workflow for treatment hierarchies in CNMA that identifies uniquely estimable relative effects in frequentist and Bayesian settings.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A workflow identifies uniquely estimable relative effects to build valid treatment hierarchies in component network meta-analysis.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"954117978b878892b0d93a6b13e003a83da8924983e71d34914052dd502b7398"},"source":{"id":"2605.15142","kind":"arxiv","version":1},"verdict":{"id":"d742324b-ec6e-4ac5-b4d6-8a601a63b964","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T03:03:54.088339Z","strongest_claim":"We present a step-by-step workflow for answering treatment hierarchy questions in both frequentist and Bayesian CNMA, including explicitly identifying the uniquely estimable relative effects.","one_line_summary":"A workflow for treatment hierarchies in CNMA that identifies uniquely estimable relative effects in frequentist and Bayesian settings.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That identifying uniquely estimable relative effects is always feasible from the network structure and sufficient to construct valid non-misleading hierarchies without further assumptions on connectivity or data.","pith_extraction_headline":"A workflow identifies uniquely estimable relative effects to build valid treatment hierarchies in component network meta-analysis."},"references":{"count":133,"sample":[{"doi":"","year":2025,"title":"Ades, A. E. and Davies, Annabel L. and Phillippo, David M. and Pedder, Hugo and Thom, Howard and Downing, Beatrice and Caldwell, Deborah M. and Welton, Nicky J. , year = 2025, journal =. Treatment Rec","work_id":"c28c2542-eea2-465b-94d9-93649e3ba019","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Ades, A. E. and Welton, Nicky J. and Dias, Sofia and Phillippo, David M. and Caldwell, Deborah M. , year = 2024, journal =. Twenty Years of Network Meta-Analysis:","work_id":"f3c82b72-c6f2-4df7-a6d7-4224cabe582b","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Electronic Journal of Statistics , volume =","work_id":"ce56d05d-ad0f-4c46-8fa1-e8e5764db821","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Al Mohamad, Diaa and Goeman, Jelle J. and. Simultaneous. Biometrics , volume =","work_id":"63418ad8-f716-44b0-abcb-726d4a394af4","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"and Premji, Zahra A","work_id":"6d4a8a5a-602a-4272-9638-bcb64c19ceb3","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":133,"snapshot_sha256":"4e02e420ace1b6ba42c654d06705fc766a9d44dd0c4ac86cdcdf5488febb20e7","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}