title: Use of generalized linear mixed models for network meta-analysis.
abstract: In the past decade, a new statistical method-network meta-analysis-has been developed to address limitations in traditional pairwise meta-analysis. Network meta-analysis incorporates all available evidence into a general statistical framework for comparisons of multiple treatments. Bayesian network meta-analysis, as proposed by Lu and Ades, also known as "mixed treatments comparisons," provides a flexible modeling framework to take into account complexity in the data structure. This article shows how to implement the Lu and Ades model in the frequentist generalized linear mixed model. Two examples are provided to demonstrate how centering the covariates for random effects estimation within each trial can yield correct estimation of random effects. Moreover, under the correct specification for random effects estimation, the dummy coding and contrast basic parameter coding schemes will yield the same results. It is straightforward to incorporate covariates, such as moderators and confounders, into the generalized linear mixed model to conduct meta-regression for multiple treatment comparisons. Moreover, this approach may be extended easily to other types of outcome variables, such as continuous, counts, and multinomial.
authors.affiliation: Institute of Epidemiology & Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan (Y-KT) yukangtu@ntu.edu.tw.
authors.forename: Yu-Kang
authors.initials: YK
authors.lastname: Tu
mesh_terms: D019317:Evidence-Based Medicine; D006801:Humans; D016014:Linear Models; D015201:Meta-Analysis as Topic; D016032:Randomized Controlled Trials as Topic / Q000379:methods
keywords: generalized linear mixed models.; mixed treatments comparisons; network meta-analysis; randomized controlled trials