Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression

By Ian R. White, J. Barrett, Dan Jackson, Julian P. T. Higgins. Research Synthesis Methods, 2012

1,033 citations recorded by the source corpus.

Network meta‐analysis (multiple treatments meta‐analysis, mixed treatment comparisons) attempts to make the best use of a set of studies comparing more than two treatments. However, it is important to assess whether a body of evidence is consistent or inconsistent. Previous work on models for network meta‐analysis that allow for heterogeneity between studies has either been restricted to two‐arm trials or followed a Bayesian framework. We propose two new frequentist ways to estimate consistency and inconsistency models by expressing them as multivariate random‐effects meta‐regressions, which can be implemented in some standard software packages. We illustrate the approach using the mvmeta package in Stata. Copyright © 2012 John Wiley & Sons, Ltd.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/687dc97c2f524aca3de109cc4e33fd761eb121a9

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Citations in the corpus

CINeMA: An approach for assessing confidence in the results of a network meta-analysis (2020, methodology): “The design-by-treatment interaction test is such a global test [45,46].”
Ranking treatments in frequentist network meta-analysis works without resampling methods (2015, neutral): “[25, 26].”
Indirect and mixed‐treatment comparison, network, or multiple‐treatments meta‐analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool (2012, methodology): “The current special issue of Research Synthesis Methods includes an article which shows how to fit network meta-analysis using the new mvmeta STATA command, accounting properly for correlations induced by multi-arm trials (White et al., 2012).”
Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies (2012, methodology): “Statistical conflicts of this nature have been termed incoherence (Lumley, 2002; Ioannidis, 2009) or inconsistency (Lu and Ades, 2006; Salanti et al., 2008) in the literature and are the principal topic of this paper.”
Visualizing Assumptions and Results in Network Meta-analysis: The Network Graphs Package (2015, methodology): “In Stata, simple indirect comparisons (using information from a single loop) can be performed using the indirect command (Miladinovic et al. 2014), and full network meta-analysis can be performed using the mvmeta (White 2011; White et al. 2012) and network 1 (White 2015) packages.”
A graphical tool for locating inconsistency in network meta-analyses (2013, methodology): “[16] have used the term inconsistency degrees of freedom for the difference in the number of parameters between the full inconsistency model and the consistency model, but Lu et al.”
GetReal in network meta‐analysis: a review of the methodology (2016, methodology): “Higgins et al. (2012) and White et al. (2012) introduced an alternative inconsistency model, the ‘design-by-treatment’ interaction model, which encompasses both loop and design inconsistencies.”
CINeMA: Software for semiautomated assessment of the confidence in the results of network meta‐analysis (2020, positive): “CINeMA performs and displays the results of two methods; the first is a global method to assess incoherence, the design-by-treatment interaction test (26,27).”
Network Meta-analysis (2015, methodology): “2 Model for network meta-analysis I brieﬂy describe the general model here; more details are given by White et al. (2012).”
Comparative efficacy and tolerability of medications for attention-deficit hyperactivity disorder in children, adolescents, and adults: a systematic review and network meta-analysis (2018, neutral): “2010;16(22):2434-2442  Gerber W-D, Gerber-von Muller G, Andrasik F, et al.”