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


Type
Article
Change log
Authors
Veroniki, Areti Angeliki 
Law, Martin 
Tricco, Andrea C 
Baker, Rose 
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.

Description
Keywords
incoherence, mixed treatment comparisons, multiple treatments meta-analysis, random-effects models, Algorithms, Alzheimer Disease, Computer Simulation, Humans, Likelihood Functions, Meta-Analysis as Topic, Models, Statistical, Network Meta-Analysis, Regression Analysis, Research Design, Treatment Outcome
Journal Title
Res Synth Methods
Conference Name
Journal ISSN
1759-2879
1759-2887
Volume Title
8
Publisher
Wiley
Sponsorship
MRC (unknown)
National Institute for Health Research (NIHR) (via Royal Brompton & Harefield NHS Foundation Trust) (unknown)