Au SCL. Understanding network meta-analysis. World J Clin Cases 2024; 12(35): 6848-6850 [DOI: 10.12998/wjcc.v12.i35.6848]
Corresponding Author of This Article
Sunny Chi Lik Au, MBChB, Chief Doctor, Surgeon, Department of Ophthalmology, Tung Wah Eastern Hospital, Lo Ka Chow Memorial Ophthalmic Centre, No. 19 Eastern Hospital Road, Causeway Bay, Hong Kong 999077, China. kilihcua@gmail.com
Research Domain of This Article
Medicine, Research & Experimental
Article-Type of This Article
Letter to the Editor
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Clin Cases. Dec 16, 2024; 12(35): 6848-6850 Published online Dec 16, 2024. doi: 10.12998/wjcc.v12.i35.6848
Understanding network meta-analysis
Sunny Chi Lik Au
Sunny Chi Lik Au, Department of Ophthalmology, Tung Wah Eastern Hospital, Hong Kong 999077, China
Author contributions: Au SCL designed the research, acquired the data, performed the analysis and interpretation of data, and drafted and revised the article; the author read and approved the final version of the manuscript to be published.
Conflict-of-interest statement: The author declares no conflict of interest in publishing the manuscript.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Sunny Chi Lik Au, MBChB, Chief Doctor, Surgeon, Department of Ophthalmology, Tung Wah Eastern Hospital, Lo Ka Chow Memorial Ophthalmic Centre, No. 19 Eastern Hospital Road, Causeway Bay, Hong Kong 999077, China. kilihcua@gmail.com
Received: March 13, 2024 Revised: September 22, 2024 Accepted: October 8, 2024 Published online: December 16, 2024 Processing time: 224 Days and 19.2 Hours
Abstract
Recently, in the World Journal of Clinical Cases, studied the different non-steroidal anti-inflammatory drugs (meloxicam, celecoxib, naproxen, and rofecoxib) for juvenile idiopathic arthritis with network meta-analysis (NMA). This manuscript aims to introduce to clinicians what NMA is. NMA represents a fundamental technique for simultaneously comparing three or more interventions within a single analysis, harnessing both direct and indirect evidence derived from a network of studies. It surpasses pair-wise meta-analysis which are confined to direct comparison of two items in clinical trials. This approach can estimate the relative effects between any pair of interventions within the network, often yielding more precise estimations than those generated from single direct or indirect analyses. NMA necessitates steps akin to those of conventional meta-analysis, involving a thorough literature search, assessment of potential trial biases, statistical analysis of reported pairwise comparisons for all relevant outcomes, and evaluation of overall certainty of evidence on an outcome-specific basis. However, NMA demands substantial resources, given its propensity to address broader inquiries, typically involving a larger number of studies at each phase of the systematic review, from screening to analysis, compared to traditional meta-analyses.
Core Tip: Network meta-analysis stands as a potent instrument for comparative research of three or more. It surpasses pair-wise meta-analysis in complexity. Additionally, supplementary analyses, such as network meta-regression further elevate the intricacy of the analysis.
