Indirect and mixed‐treatment comparison, network, or multiple‐treatments meta‐analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool

TLDR

The growing number of treatment options and clinical trials has shifted systematic reviews toward network meta‑analysis, which, while promising for comparing multiple interventions, faces criticism over the assumptions linking direct and indirect evidence. The article aims to present and clarify the assumptions underlying network meta‑analysis, discuss methodological considerations, and summarize recent progress. They illustrate the statistical and nonstatistical assumptions of network meta‑analysis through multiple formulations and analogies to earlier meta‑analysis. © 2012 John Wiley & Sons, Ltd.

Abstract

The ever increasing number of alternative treatment options and the plethora of clinical trials have put systematic reviews and meta-analysis under a new perspective by emphasizing the need to make inferences about competing treatments for the same condition. The statistical component in reviews that compare multiple interventions, network meta-analysis, is the next generation evidence synthesis toolkit which, when properly applied, can serve decision-making better than the established pairwise meta-analysis. The criticism and enthusiasm for network meta-analysis echo those that greeted the advent of simple meta-analysis. The main criticism is associated with the difficulty in evaluating the assumption underlying the statistical synthesis of direct and indirect evidence. In the present article, the assumption of the network meta-analysis are presented using various formulations, the statistical and nonstatistical methodological considerations are elucidated, and the progress achieved in this field is summarized. Throughout, focus is put on highlighting the analogy between the concerns and difficulties that the scientific community had some time ago when advancing from individual trials to their quantitative synthesis via meta-analysis and those currently expressed about the transition from head-to-head meta-analyses to network meta-analysis. Copyright © 2012 John Wiley & Sons, Ltd.