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Despite the growing popularity of NMA within comparative effectiveness research, it comes with potential challenges. For example, within-study correlations among treatment comparisons are rarely reported in the published literature. Yet, these correlations are pivotal for valid statistical inference. As demonstrated in earlier studies, ignoring these correlations can inflate mean squared errors of the resulting point estimates and lead to inaccurate standard error estimates. This article introduces a composite likelihood-based approach that ensures accurate statistical inference without requiring knowledge of the within-study correlations. The proposed method is computationally robust and efficient, with substantially reduced computational time compared to the state-of-the-science methods implemented in R packages. The proposed method was evaluated through extensive simulations and applied to two important applications including an NMA comparing interventions for primary open-angle glaucoma, and another comparing treatments for chronic prostatitis and chronic pelvic pain syndrome.<\/jats:p>","DOI":"10.1017\/rsm.2024.12","type":"journal-article","created":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T10:43:16Z","timestamp":1742208196000},"page":"272-290","source":"Crossref","is-referenced-by-count":2,"title":["Network meta-analysis made simple: A composite likelihood approach"],"prefix":"10.1017","volume":"16","author":[{"given":"Yu-Lun","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haitao","family":"Chu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2025,3,17]]},"reference":[{"key":"S1759287924000127_r12","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6752"},{"key":"S1759287924000127_r30","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3165"},{"key":"S1759287924000127_r42","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6789"},{"key":"S1759287924000127_r23","doi-asserted-by":"publisher","DOI":"10.1214\/21-AOAS1469"},{"key":"S1759287924000127_r19","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v080.i05"},{"key":"S1759287924000127_r40","doi-asserted-by":"publisher","DOI":"10.1080\/07350015.2016.1247004"},{"key":"S1759287924000127_r17","doi-asserted-by":"publisher","DOI":"10.1198\/016214505000001302"},{"key":"S1759287924000127_r39","unstructured":"[39] Pustejovsky, J. clubSandwich: Cluster-robust (sandwich) variance estimators with small-sample corrections. 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