Characteristics of networks of interventions: a description of a database of 186 published networks.
Systematic reviews that employ network meta-analysis are undertaken and published with increasing frequency while related statistical methodology is evolving. Future statistical developments and evaluation of the existing methodologies could be motivated by the characteristics of the networks of interventions published so far in order to tackle real rather than theoretical problems. Based on the recently formed network meta-analysis literature we aim to provide an insight into the characteristics of networks in healthcare research. We searched PubMed until end of 2012 for meta-analyses that used any form of indirect comparison. We collected data from networks that compared at least four treatments regarding their structural characteristics as well as characteristics of their analysis. We then conducted a descriptive analysis of the various network characteristics. We included 186 networks of which 35 (19%) were star-shaped (treatments were compared to a common comparator but not between themselves). The median number of studies per network was 21 and the median number of treatments compared was 6. The majority (85%) of the non-star shaped networks included at least one multi-arm study. Synthesis of data was primarily done via network meta-analysis fitted within a Bayesian framework (113 (61%) networks). We were unable to identify the exact method used to perform indirect comparison in a sizeable number of networks (18 (9%)). In 32% of the networks the investigators employed appropriate statistical methods to evaluate the consistency assumption; this percentage is larger among recently published articles. Our descriptive analysis provides useful information about the characteristics of networks of interventions published the last 16 years and the methods for their analysis. Although the validity of network meta-analysis results highly depends on some basic assumptions, most authors did not report and evaluate them adequately. Reviewers and editors need to be aware of these assumptions and insist on their reporting and accuracy.
Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
Adriani
A
Nikolakopoulou
Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
Anna
A
Chaimani
Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
Areti Angeliki
AA
Veroniki
Department of Orthopaedics, University of Ioannina School of Medicine, Ioannina, Greece ; Molecular Cell Biology and Regenerative Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Haris S
HS
Vasiliadis
Center for Evidence Based Medicine and Department of Biostatistics, Program in Public Health, Brown University, Providence, Rhode Island, United States of America.
Christopher H
CH
Schmid
Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.
Georgia
G
Salanti
D001499:Bayes Theorem; D035843:Biomedical Research / Q000379:methods*; D015201:Meta-Analysis as Topic; D016017:Odds Ratio; D012107:Research Design / Q000706:statistics & numerical data*