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2017 ; 17
(1
): 104
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Using structural equation modeling for network meta-analysis
#MMPMID28709406
Tu YK
; Wu YC
BMC Med Res Methodol
2017[Jul]; 17
(1
): 104
PMID28709406
show ga
BACKGROUND: 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.
METHODS: 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. RESULTS: 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.
CONCLUSION: 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.
|*Algorithms
[MESH]
|*Least-Squares Analysis
[MESH]
|*Models, Statistical
[MESH]
|*Network Meta-Analysis
[MESH]
|Bayes Theorem
[MESH]
|Hemorrhage/complications/prevention & control
[MESH]
|Humans
[MESH]
|Linear Models
[MESH]
|Liver Cirrhosis/complications/therapy
[MESH]
|Multivariate Analysis
[MESH]
|Outcome Assessment, Health Care/methods/statistics & numerical data
[MESH]
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