Work

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

Year: 2012

Type: article

Abstract: 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.

Source: Research Synthesis Methods

DOI: 10.1002/jrsm.1045

Authors: Ian R. White, Jessica K. Barrett, Dan Jackson, Julian P. T. Higgins

Institutions: MRC Biostatistics Unit, University of York

Language: English

Topic: Meta-analysis and systematic reviews

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: bronze

Funders: National Science Foundation, British Heart Foundation, Medical Research Council, Division of Mathematical Sciences

Awards: Statistical and Applied Mathematical Sciences Institute, G5238254024, G5272147299, G6647857051, RENEWAL: Systematic approaches to the evaluation of emerging coronary risk markers. Large-scale epidemiological analyses of existing data and stored biological samples