Network meta-analysis (NMA) is a type of meta-analysis that adds an additional variable to a meta-analysis, and instead of a simple summation of trials that have evaluated the same treatment, several different treatments are compared by statistical inference.1 NMA is also referred to as mixed treatments comparison or multiple treatments comparison meta-analysis.2,3,4

It was recognised at the National Institute for Clinical Excellence (NICE) that there is an increasing need for technology appraisals and clinical guidelines to be informed by integrated analyses, because of the lack of sufficient head to head comparisons of new treatments to inform clinical practice.5 Literature suggests that NMA is a feasible option to inform clinical practice decisions, particularly in cases where several treatments are examined.6,7

NMA includes a combination of direct evidence within the trials and indirect evidence across the trials, thereby providing estimates of relative efficacy between all the relevant interventions, even in cases where there has never been a head to head comparison.1,2,3 In essence, the treatment effects are calculated for all treatments or interventions using all the available evidence in one simultaneous analysis. 6,8

NMA relies on two main assumptions; homogeneity of compared trials and consistency in direct and indirect evidence.7,9 A simple example of a NMA would be as follows. A trial compares drug A to drug B and another trial, including the same target patient population, compares drug B to drug C.  Assuming that drug A is superior to drug B in the first trial, and assuming drug B is equivalent to drug C in a second trial, the NMA allows a potential inference that statistically drug A is also superior to drug C for this particular target population.1,4,8 Therefore; one can say that if drug A is more effective than drug B, and drug B is equivalent to drug C, then drug A is also more effective drug C. 1,4,7

The main advantage of NMA over traditional or pairwise meta-analysis is that it enables some certainty about all treatment comparisons based on the strength of indirect evidence, and it further allows an estimation of the comparative effects, which would not have been examined in parallel group randomized clinical trials.2,4 Overall, NMA potentially enable an assessment of the benefits and harms for more than two interventions for the same clinical condition.

In terms of limitations with NMA, this type of meta-analysis is more likely to be valid when analysing sufficiently homogenous studies that include very similar patient populations.  As NMA increases the number and type of studies being compared and combined, there is more likelihood of studies getting combined, which are heterogeneous.1,3,9  In addition, the various overlapping meta-analyses with heterogeneous findings may potentially confound the readers and decision makers. Further, NMA from a practical point of view is more complex than the conventional pair-wise meta-analysis, and requires more time and resources. The various assumptions underlying conventional pairwise meta-analyses are well researched and understood; however, the assumptions related to NMA are seen to be more complex, leading to misinterpretations.

The methodological work to address the limitations of NMAs is an on-going work, and in light of this fact, researchers and end-users should be cautious when interpreting results from NMAs, as inappropriate combination of studies may result in overestimation of treatment effects and therefore misleading results, with some uncertainty in improving patient outcomes! Nevertheless, NMAs are seen as useful tools that are increasingly becoming attractive because they provide a comprehensive framework for decision-making.

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  1. Cipriani A, Higgins JP, Geddes JR, Salanti G. Conceptual and technical challenges in network meta-analysis. Ann Intern Med. 2013 Jul 16; 159(2):130-7.
  2. Mills EJ, Thorlund K, Ioannidis JP. Demystifying trial networks and network meta-analysis. BMJ. 2013 May 14; 346: f2914.
  3. National Institute for Health and Care Excellence. Guide to the Methods of Technology Appraisal 2013 [Internet]. London: National Institute for Health and Care Excellence (NICE); 2013 Apr. Process and Methods Guides No. 9. NICE Process and Methods Guides. [Viewed on 02/08/2018]
  4. Li T, Puhan MA, Vedula SS, Singh S, Dickersin K; Ad Hoc Network Meta-analysis Methods Meeting Working Group. Network meta-analysis-highly attractive but more methodological research is needed. BMC Med. 2011 Jun 27; 9:79.
  5. Rawlins MD. In pursuit of quality: the National Institute for Clinical Excellence. Lancet. 1999; 353:1079–82.
  6. Caldwell DM, Ades AE, Higgins JP. Simultaneous comparison of multiple treatments: combining direct and indirect evidence. BMJ. 2005; 331(7521):897–900.
  7. Tu YK, Faggion CM Jr. A primer on network meta-analysis for dental research. ISRN Dent. 2012; 2012:276520.
  8. Sutton A, Ades AE, Cooper N, Abrams K. Use of indirect and mixed treatment comparisons for technology assessment. Pharmacoeconomics. 2008; 26(9):753–767.
  9. Donegan S, Williamson P, D’Alessandro U, Tudur Smith C. Assessing key assumptions of network meta-analysis: a review of methods. Res Synth Methods. 2013 Dec; 4(4):291-323.

Written By – Dr. Sandeep Moola (Research Fellow, The University of Adelaide, Australia)

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