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• W2147468991 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." @default.
• W2147468991 created "2016-06-24" @default.
• W2147468991 creator A5042622898 @default.
• W2147468991 date "2014-09-26" @default.
• W2147468991 modified "2023-10-18" @default.
• W2147468991 title "Use of Generalized Linear Mixed Models for Network Meta-analysis" @default.
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• W2147468991 doi "https://doi.org/10.1177/0272989x14545789" @default.
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