BACKGROUND: Network meta-analysis is becoming increasingly popular for 
establishing comparative effectiveness among multiple interventions for the same 
disease. Network meta-analysis inherits all methodological challenges of 
standard pairwise meta-analysis, but with increased complexity due to the 
multitude of intervention comparisons. One issue that is now widely recognized 
in pairwise meta-analysis is the issue of sample size and statistical power. 
This issue, however, has so far only received little attention in network 
meta-analysis. To date, no approaches have been proposed for evaluating the 
adequacy of the sample size, and thus power, in a treatment network.
FINDINGS: In this article, we develop easy-to-use flexible methods for 
estimating the 'effective sample size' in indirect comparison meta-analysis and 
network meta-analysis. The effective sample size for a particular treatment 
comparison can be interpreted as the number of patients in a pairwise 
meta-analysis that would provide the same degree and strength of evidence as 
that which is provided in the indirect comparison or network meta-analysis. We 
further develop methods for retrospectively estimating the statistical power for 
each comparison in a network meta-analysis. We illustrate the performance of the 
proposed methods for estimating effective sample size and statistical power 
using data from a network meta-analysis on interventions for smoking cessation 
including over 100 trials.
CONCLUSION: The proposed methods are easy to use and will be of high value to 
regulatory agencies and decision makers who must assess the strength of the 
evidence supporting comparative effectiveness estimates.