Is Network Meta-analysis a Revolutionary Statistical Tool for Improving the Reliability of Clinical Trial Results? A Brief Overview and Emerging Issues Arising

Abstract

Network meta-analysis (NMA) as the quantification of pairwise meta-analysis in a network format has been of particular interest to medical researchers in recent years. As a powerful tool with which direct and indirect evidence from multiple interventions can be synthesized simultaneously in the study and design of clinical trials, NMA enables inferences to be drawn about the relative effect of drugs that have never been compared. In this way, NMA provides information on the hierarchy of competing interventions for a given disease concerning clinical effectiveness, thus giving clinicians a comprehensive picture for decision-making and potential avoidance of additional costs. However, estimates of treatment effects derived from the results of network meta-analyses should be interpreted with due consideration of their uncertainty, because simple scores or treatment probabilities may be misleading. This is particularly true where, given the complexity of the evidence, there is a serious risk of misinterpretation of information from aggregated data sets. For these reasons, NMA should be performed and interpreted by both expert clinicians and experienced statisticians, while a more comprehensive search of the literature and a more careful evaluation of the body of evidence can maximize the transparency of the NMA and potentially avoid errors in its interpretation. This review provides the key concepts as well as the challenges we face when studying a network meta-analysis of clinical trials.

Keywords: Bayesian network models; Markov Chain Monte Carlo algorithms; Network meta-analysis; indirect evidence; review; treatment effects; treatment network.