Paule-Mandel estimators for network meta-analysis with random inconsistency effects

Jackson, D, Veroniki, AH, Law, M, Tricco, AC and Baker, RD 2017, 'Paule-Mandel estimators for network meta-analysis with random inconsistency effects' , Research Synthesis Methods .

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Abstract

Network meta-analysis is used to simultaneously compare multiple treatments in a single analysis. However, network meta-analyses may exhibit inconsistency, where direct and different forms of indirect evidence are not in agreement with each other, even after allowing for between-study heterogeneity. Models for network meta-analysis with random inconsistency effects have the dual aim of allowing for inconsistencies and estimating average treatment effects across the whole network. To date, two classical estimation methods for fitting this type of model have been developed: a method of moments that extends DerSimonian and Laird's univariate method and maximum likelihood estimation. However, the Paule and Mandel estimator is another recommended classical estimation method for univariate meta-analysis. In this paper, we extend the Paule and Mandel method so that it can be used to fit models for network meta-analysis with random inconsistency effects. We apply all three estimation methods to a variety of examples that have been used previously and we also examine a challenging new dataset that is highly heterogenous. We perform a simulation study based on this new example. We find that the proposed Paule and Mandel method performs satisfactorily and generally better than the previously proposed method of moments because it provides more accurate inferences. Furthermore, the Paule and Mandel method possesses some advantages over likelihood-based methods because it is both semiparametric and requires no convergence diagnostics. Although restricted maximum likelihood estimation remains the gold standard, the proposed methodology is a fully viable alternative to this and other estimation methods.

Item Type: Article
Schools: Schools > Salford Business School > Salford Business School Research Centre
Journal or Publication Title: Research Synthesis Methods
Publisher: Wiley
ISSN: 1759-2879
Related URLs:
Depositing User: Prof Rose Dawn Baker
Date Deposited: 09 Jun 2017 08:04
Last Modified: 09 Jun 2017 08:04
URI: http://usir.salford.ac.uk/id/eprint/42549

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