title: Unsolved issues of mixed treatment comparison meta-analysis: network size and inconsistency.
abstract: Indirect comparisons and mixed treatment comparison (MTC) meta-analyses are increasingly used in medical research. These methods allow a simultaneous analysis of all relevant interventions in a connected network even if direct evidence regarding two interventions is missing. The framework of MTC meta-analysis provides a flexible approach for complex networks. However, this method has yet some unsolved problems, in particular the choice of the network size and the assessment of inconsistency. In this paper, we describe the practical application of MTC meta-analysis by using a data set on antidepressants. We focus on the impact of the size of the chosen network and the assumption of consistency. A larger network is based on more evidence but may show inconsistencies, whereas a smaller network contains less evidence but may show no clear inconsistencies. A choice is required which network should be used in practice. In summary, MTC meta-analysis represents a promising approach; however, clear application standards are still lacking. Especially, standards for the identification of inconsistency and the way to deal with potential inconsistency are required. Copyright © 2012 John Wiley & Sons, Ltd.
authors.affiliation: Department of Medical Biometry, Institute for Quality and Efficiency in Health Care (IQWiG), Cologne, Germany. Sibylle.Sturtz@iqwig.de.
authors.forename: Sibylle
authors.initials: S
authors.lastname: Sturtz
authors.affiliation: Department of Medical Biometry, Institute for Quality and Efficiency in Health Care (IQWiG), Cologne, Germany.|Faculty of Medicine, University of Cologne, Cologne, Germany.
authors.forename: Ralf
authors.initials: R
authors.lastname: Bender
keywords: indirect comparison; mixed treatment comparison (MTC) meta-analysis; model assumptions