Network meta-analysis has been established in recent years as a particularly useful evidence synthesis tool. However, it is still challenging to develop understandable and concise ways to present data, assumptions, and results from network meta-analysis to inform decision making and evaluate the credibility of the results. In this article, we provide a suite of commands with graphical tools to facilitate the understanding of data, the evaluation of assumptions, and the interpretation of findings from network meta-analysis.
AnscombeF. J.1973. Graphs in statistical analysis. American Statistician27: 17–21.
2.
Anzures-CabreraJ., and HigginsJ. P. T.2010. Graphical displays for meta-analysis: An overview with suggestions for practice. Research Synthesis Methods1: 66–80.
3.
BaxL., IkedaN., FukuiN., YajuY., TsurutaH., and MoonsK. G.2009. More than numbers: the power of graphs in meta-analysis. American Journal of Epidemiology169: 249–255.
4.
BucherH. C., GuyattG. H., GriffithL. E., and WalterS. D.1997. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. Journal of Clinical Epidemiology50: 683–691.
5.
CaldwellD. M., AdesA. E., and HigginsJ. P.2005. Simultaneous comparison of multiple treatments: Combining direct and indirect evidence. British Medical Journal331: 897–900.
6.
ChaimaniA., HigginsJ. P. T., MavridisD., SpyridonosP., and SalantiG.2013a. Graphical tools for network meta-analysis in Stata. PLOS ONE8: e76654.
7.
ChaimaniA., MavridisD., and SalantiG.2014. A hands-on practical tutorial on performing meta-analysis with Stata. Evidence-Based Mental Health17: 111–116.
8.
ChaimaniA., and SalantiG.2012. Using network meta-analysis to evaluate the existence of small-study effects in a network of interventions. Research Synthesis Methods3: 161–176.
9.
ChaimaniA., VasiliadisH. S., PandisN., SchmidC. H., WeltonN. J., and SalantiG.2013b. Effects of study precision and risk of bias in networks of interventions: A network meta-epidemiological study. International Journal of Epidemiology42: 1120–1131.
10.
CiprianiA., FurukawaT. A., SalantiG., GeddesJ. R., HigginsJ. P., ChurchillR., WatanabeN., NakagawaA., OmoriI. M., McGuireH., TansellaM., and BarbuiC.2009. Comparative efficacy and acceptability of 12 new-generation antidepressants: A multiple-treatments meta-analysis. Lancet373: 746–758.
11.
CooperH., HedgesL. V., and ValentineJ. C.2009. The Handbook of Research Synthesis and Meta-Analysis. 2nd ed. New York: Russell Sage Foundation.
12.
DerSimonianR., and LairdN.1986. Meta-analysis in clinical trials. Controlled Clinical Trials7: 177–188.
13.
DiasS., WeltonN. J., CaldwellD. M., and AdesA. E.2010. Checking consistency in mixed treatment comparison meta-analysis. Statistics in Medicine29: 932–944.
14.
ElliottW. J., and MeyerP. M.2007. Incident diabetes in clinical trials of antihypertensive drugs: A network meta-analysis. Lancet369: 201–207.
15.
HandlJ., KnowlesJ., and KellD. B.2005. Computational cluster validation in post-genomic data analysis. Bioinformatics21: 3201–3212.
16.
HigginsJ. P. T., JacksonD., BarrettJ. K., LuG., AdesA. E., and WhiteI. R.2012. Consistency and inconsistency in network meta-analysis: Concepts and models for multi-arm studies. Research Synthesis Methods3: 98–110.
17.
HigginsJ. P. T., ThompsonS. G., and SpiegelhalterD. J.2009. A re-evaluation of random-effects meta-analysis. Journal of the Royal Statistical Society, Series A172: 137–159.
18.
JansenJ. P., and NaciH.2013. Is network meta-analysis as valid as standard pairwise meta-analysis? It all depends on the distribution of effect modifiers. BMC Medicine11: 159.
19.
JungY., ParkH., DuD.-Z., and DrakeB. L.2003. A decision criterion for the optimal number of clusters in hierarchical clustering. Journal of Global Optimization25: 91–111.
20.
KaufmanL., and RousseeuwP. J.2005. Finding Groups in Data: An Introduction to Cluster Analysis.Hoboken, NJ: Wiley.
21.
KrahnU., BinderH., and KönigJ.2013. A graphical tool for locating inconsistency in network meta-analyses. BMC Medical Research Methodology13: 35.
22.
LeeA. W.2014. Review of mixed treatment comparisons in published systematic reviews shows marked increase since 2009. Journal of Clinical Epidemiology67: 138–143.
23.
LuG., and AdesA. E.2006. Assessing evidence inconsistency in mixed treatment comparisons. Journal of the American Statistical Association101: 447–459.
24.
LuG., WeltonN. J., HigginsJ. P. T., WhiteI. R., and AdesA. E.2011. Linear inference for mixed treatment comparison meta-analysis: A two-stage approach. Research Synthesis Methods2: 43–60.
25.
LunnD. J., ThomasA., BestN., and SpiegelhalterD.2000. WinBUGS—A Bayesian modelling framework: Concepts, structure and extensibility. Statistics and Computing10: 325–337.
26.
MiladinovicB., ChaimaniA., HozoI., and DjulbegovicB.2014. Indirect treatment comparison. Stata Journal14: 76–86.
27.
MorenoS. G., SuttonA. J., AdesA. E., CooperN. J., and AbramsK. R.2011. Adjusting for publication biases across similar interventions performed well when compared with gold standard data. Journal of Clinical Epidemiology64: 1230–1241.
28.
MorrisC. N.1983. Parametric empirical Bayes inference: Theory and applications. Journal of the American Statistical Association78: 47–55.
29.
NikolakopoulouA., ChaimaniA., VeronikiA. A., VasiliadisH. S., SchmidC. H., and SalantiG.2014. Characteristics of networks of interventions: A description of a database of 186 published networks. PLOS ONE9: e86754.
30.
PhungO. J., ScholleJ. M., TalwarM., and ColemanC. I.2010. Effect of noninsulin antidiabetic drugs added to metformin therapy on glycemic control, weight gain, and hypoglycemia in type 2 diabetes. Journal of the American Medical Association303: 1410–1418.
31.
RileyR. D., HigginsJ. P. T., and DeeksJ. J.2011. Interpretation of random effects meta-analyses. British Medical Journal342: d549.
32.
SalantiG.2012. Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: Many names, many benefits, many concerns for the next generation evidence synthesis tool. Research Synthesis Methods3: 80–97.
33.
SalantiG., AdesA. E., and IoannidisJ. P.2011. Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: An overview and tutorial. Journal of Clinical Epidemiology64: 163–171.
34.
SalantiG., DiasS., WeltonN. J., AdesA. E., GolfinopoulosV., KyrgiouM., MauriD., and IoannidisJ. P.2010. Evaluating novel agent effects in multiple-treatments meta-regression. Statistics in Medicine29: 2369–2383.
35.
SalantiG., GiovaneC. D., ChaimaniA., CaldwellD. M., and HigginsJ. P. T.2014. Evaluating the quality of evidence from a network meta-analysis. PLOS ONE9: e99682.
36.
SongF., ClarkA., BachmannM. O., and MaasJ.2012. Simulation evaluation of statistical properties of methods for indirect and mixed treatment comparisons. BMC Medical Research Methodology12: 138.
37.
ThijsV., LemmensR., and FieuwsS.2008. Network meta-analysis: Simultaneous meta-analysis of common antiplatelet regimens after transient ischaemic attack or stroke. European Heart Journal29: 1086–1092.
38.
VeronikiA. A., MavridisD., HigginsJ. P. T., and SalantiG.2014. Characteristics of a loop of evidence that affect detection and estimation of inconsistency: A simulation study. BMC Medical Research Methodology14: 106.
39.
VeronikiA. A., VasiliadisH. S., HigginsJ. P., and SalantiG.2013. Evaluation of inconsistency in networks of interventions. International Journal of Epidemiology42: 332–345.
40.
ViechtbauerW.2005. Bias and efficiency of meta-analytic variance estimators in the random-effects model. Journal of Educational and Behavioral Statistics30: 261–293.
41.
WhiteI. R.2011. Multivariate random-effects meta-regression: Updates to mvmeta. Stata Journal11: 255–270.
42.
WhiteI. R.2015. Network meta-analysis. Stata Journal15: 951–984.
43.
WhiteI. R., BarrettJ. K., JacksonD., and HigginsJ. P. T.2012. Consistency and inconsistency in network meta-analysis: Model estimation using multivariate meta-regression. Research Synthesis Methods3: 111–125.
