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Using structural equation modeling for network meta-analysis

Overview of attention for article published in BMC Medical Research Methodology, July 2017
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28 Mendeley
Title
Using structural equation modeling for network meta-analysis
Published in
BMC Medical Research Methodology, July 2017
DOI 10.1186/s12874-017-0390-9
Pubmed ID
Authors

Yu-Kang Tu, Yun-Chun Wu

Abstract

Network meta-analysis overcomes the limitations of traditional pair-wise meta-analysis by incorporating all available evidence into a general statistical framework for simultaneous comparisons of several treatments. Currently, network meta-analyses are undertaken either within the Bayesian hierarchical linear models or frequentist generalized linear mixed models. Structural equation modeling (SEM) is a statistical method originally developed for modeling causal relations among observed and latent variables. As random effect is explicitly modeled as a latent variable in SEM, it is very flexible for analysts to specify complex random effect structure and to make linear and nonlinear constraints on parameters. The aim of this article is to show how to undertake a network meta-analysis within the statistical framework of SEM. We used an example dataset to demonstrate the standard fixed and random effect network meta-analysis models can be easily implemented in SEM. It contains results of 26 studies that directly compared three treatment groups A, B and C for prevention of first bleeding in patients with liver cirrhosis. We also showed that a new approach to network meta-analysis based on the technique of unrestricted weighted least squares (UWLS) method can also be undertaken using SEM. For both the fixed and random effect network meta-analysis, SEM yielded similar coefficients and confidence intervals to those reported in the previous literature. The point estimates of two UWLS models were identical to those in the fixed effect model but the confidence intervals were greater. This is consistent with results from the traditional pairwise meta-analyses. Comparing to UWLS model with common variance adjusted factor, UWLS model with unique variance adjusted factor has greater confidence intervals when the heterogeneity was larger in the pairwise comparison. The UWLS model with unique variance adjusted factor reflects the difference in heterogeneity within each comparison. SEM provides a very flexible framework for univariate and multivariate meta-analysis, and its potential as a powerful tool for advanced meta-analysis is still to be explored.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 25%
Student > Doctoral Student 5 18%
Student > Ph. D. Student 5 18%
Lecturer 3 11%
Student > Postgraduate 1 4%
Other 7 25%
Readers by discipline Count As %
Unspecified 6 21%
Psychology 5 18%
Medicine and Dentistry 4 14%
Engineering 3 11%
Mathematics 3 11%
Other 7 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 June 2018.
All research outputs
#8,223,302
of 13,116,247 outputs
Outputs from BMC Medical Research Methodology
#869
of 1,203 outputs
Outputs of similar age
#150,903
of 261,636 outputs
Outputs of similar age from BMC Medical Research Methodology
#1
of 1 outputs
Altmetric has tracked 13,116,247 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,203 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.8. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
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