Title

Visualizing assumptions and results in network meta-analysis: the network graphs package

Description

The network graphs package provides a suite of commands producing graphical tools for network meta-analysis.

List of commands

networkplot Draw the plot of a network in terms of nodes and edges

netweight Estimate the contribution of direct comparisons in network estimates

ifplot Assess the presence of statistical inconsistency

netfunnel Draw the comparison-adjusted funnel plot

intervalplot Draw a forest plot with the network estimates and their confidence and predictive intervals

netleague Produce a 'league table' with the network estimates for all pairwise relative effects

sucra Estimate the relative ranking of treatmetns using probabilities

mdsrank Estimate the relative ranking of treatmetns using multidimensional scaling

clusterank Present the relative ranking of treatmetns for two outcomes

Updates

You can get the latest version of this package from net from https://www.cer-methods.com/Stata. To run the commands the latest versions of metan, metareg and mvmeta or network are required.

Examples

- Load and prepare appropriately the data for efficacy of the antidiabetics network (Phung, 2010):

. use "https://www.cer-methods.com/Stata/antidiabetics_efficacy_wide.dta", clear

(click to run)

. network import, tr(t1 t2) eff(ES) study(id) stderr(seES)

(click to run)

- Produce the plot of the network, giving information on the network structure, the number of studies evaluating each intervention,
the precision of the direct estimate for each pairwise comparison and the average bias level for every comparison with respect to blinding:

. gen invvarES=1/(seES^2)

(click to run)

. networkplot t1 t2, edgew(invvarES) edgecol(by blinding mean) edgesc(1.2) asp(0.8) lab(Placebo Sulfonylurea "DPP-4 inhibitor" Thiazolidinedione "GLP-1 analog" AGI Glinine)

(click to run)

- Load and prepare appropriately the data of the antiplatelet regimens network (Thijs, 2008):

. use "https://www.cer-methods.com/Stata/antiplatelet.dta", clear

(click to run)

. network import, tr(t1 t2) eff(ES) study(id) stderr(seES)

(click to run)

- Produce the contribution plot of the network:

. netweight ES seES t1 t2,asp(0.9) notab

(click to run)

- Considering that study risk of bias might be associated with their year of publication,
produce a bar-graph showing how much information comes from high, unclear and low risk of bias studies for each network estimate

. netweight ES seES t1 t2,bar(by rob mean) notab

(click to run)

- Assess the presence of inconsistency in every closed loop of the network:

. ifplot ES seES t1 t2 id, eform plotopt(texts(180)) xlab(1,1.3,1.8) notab

(click to run)

- Load and prepare appropriately the data for efficacy of the antidiabetics network:

. use "https://www.cer-methods.com/Stata/antidiabetics_efficacy_wide.dta", clear

(click to run)

. network import, tr(t1 t2) eff(ES) study(id) stderr(seES)

(click to run)

- Assess whether small and large trials tend to give different efficacy results focusing on the comparisons of all active treatments against placebo:

. netfunnel ES seES t1 t2 if t2=="1", ylab(0 0.1 0.2 0.3)

(click to run)

- Load the data of the antihypertensives network (Elliott, 2007):

. use "https://www.cer-methods.com/Stata/antihypertensives.dta", clear

(click to run)

- Perform network meta-analysis to obtain the network estimates of the relative effects and then estimate and plot the predictive intervals:

. network setup r n, stud(id) trt(t) num ref(1)

(click to run)

. network meta c

(click to run)

. intervalplot, eform pred null(1) lab(Placebo BB Diuretics CCB ACE ARB) marg(10 40 5 5) notab

(click to run)

- Then, produce the league table of the network ordering the treatments according to their relative ranking based on the SUCRA percentages:

. netleague, lab(Placebo BB Diuretics CCB ACE ARB) sort(ARB ACE Placebo CCB BB Diuretics) eform

(click to run)

- Run again the network meta-analysis model and obtain the ranking probabilities for all competing treatments in the network. Then, produce the rankograms:

. network rank min,zero all reps(10000) gen(prob)

(click to run)

. sucra prob*, lab(Placebo BB Diuretics CCB ACE ARB) rankog

(click to run)

- Run again the network meta-analysis model and obtain the predictive ranking probabilities.
Then, compare the cumulative ranking plots based on the estimated and the predictive ranking probabilities:

. network rank min,zero all reps(10000) gen(pred_prob) predict

(click to run)

. sucra prob*, lab(Placebo BB Diuretics CCB ACE ARB) comp(pred_prob*) n("Estimated probabilities" "Predictive probabilities")

(click to run)

- Obtain the estimated relative effects in the dataset and estimate the relative ranking of treatments using the multidimensional scaling method:

. mdsrank,best(max)

(click to run)

- Obtain the SUCRA percentages for efficacy for the antidiabetics network:

. use "https://www.cer-methods.com/Stata/antidiabetics_efficacy_long.dta", clear

(click to run)

. network setup y sd n, stud(id) trt(t) ref(1) num

(click to run)

. network meta c

(click to run)

. network rank min,zero all reps(10000) gen(eff_prob)

(click to run)

. sucra eff_prob*, lab(Placebo Sulfonylurea "DPP-4 inhibitor" Thiazolidinedione "GLP-1 analog" AGI Glinine) noplot

(click to run)

- Obtain the SUCRA percentages for tolerability for the antidiabetics network:

. use "https://www.cer-methods.com/Stata/antidiabetics_tolerability.dta", clear

(click to run)

. network setup y sd n, stud(id) trt(t) ref(1) num

(click to run)

. network meta c

(click to run)

. network rank min,zero all reps(10000) gen(tol_prob)

(click to run)

. sucra tol_prob*, lab(Placebo Sulfonylurea "DPP-4 inhibitor" Thiazolidinedione "GLP-1 analog" AGI Glinine) noplot

(click to run)

- Load the dataset that contains the SUCRA percentages for efficacy and tolerability for the antidiabetics network:

. use "https://www.cer-methods.com/Stata/antidiabeticsSUCRAS.dta", clear

(click to run)

- Present jointly the relative ranking of treatments for efficacy and acceptability:

. clusterank outcome1 outcome2 t

(click to run)

** Note: Additional examples on the use of the commands can be found here.

Limitations

For large networks (e.g. icluding more than 10 treatments) the netweight command sometimes cannot produce the contribution plot and only gives the output results.

Authors

Anna Chaimani, University of Ioannina School of Medicine, email: anna.chaimani@inserm.fr

Georgia Salanti, University of Ioannina School of Medicine

Acknowledgements

Drs Julian Higgins, Dimitris Mavridis, Ian White, Panagiota Spyridonos and Deborah Caldwell provided very helpful comments on earlier versions of the commands.

References

Bucher HC, Guyatt GH, Griffith LE, Walter SD. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J Clin Epidemiol 1997, 50(6):683-91.
(link to paper)

Caldwell DM, Ades AE, Higgins JPT. Simultaneous comparison of multiple treatments: combining direct and indirect evidence. BMJ 2005, 331(7521):897-900.
(link to paper)

Chaimani A, Salanti G. Using network meta-analysis to evaluate the existence of small-study effects in a network of interventions. Res Synth Meth 2012;3(2):161-176.
(link to paper)

Chaimani A, Higgins JPT, Mavridis D, Spyridonos P, Salanti G. Graphical tools for network meta-analysis in STATA. Plos One 8(10): e76654.
(link to paper)

Cooper H, Hedges LV, Valentine JC. The Handbook of Research Synthesis and Meta-Analysis. Russell Sage Foundation. 2009
(link to paper)

Elliott WJ, Meyer PM. Incident diabetes in clinical trials of antihypertensive drugs: a network meta-analysis. Lancet 2007, 369:201–207.
(link to paper)

Handl J, Knowles J, Kell DB. Computational cluster validation in post-genomic data analysis. Bioinformatics 2005, 21(15):3201-3212.
(link to paper)

Higgins JPT, Thompson SG, Spiegelhalter DJ. A re-evaluation of random-effects meta-analysis. J R Stat Soc Ser A 2009, 172: 137-159.
(link to paper)

Jansen JP, Naci H. Is network meta-analysis as valid as standard pairwise meta-analysis? It all depends on the distribution of effect modifiers. BMC Med 2013, 11:159.
(link to paper)

Jung Y, Park H, Du DZ, Drake BL. A decision criterion for the optimal number of clusters in hierarchical clustering. J Glob Optim 2003, 25(1):91-111.
(link to paper)

Krahn U, Binder H, Konig J. A graphical tool for locating inconsistency in network meta-analyses. BMC Med Res Methodol 2013, 13:35.
(link to paper)

Lu G, Welton NJ, Higgins JPT, White IR, Ades AE. Linear inference for mixed treatment comparison meta-analysis: A two-stage approach. Res Synth Meth 2011, 2:43-60.
(link to paper)

Lunn DJ, THomas A, Best N, Spiegelhalter DJ. WinBUGS - a Bayesian modelling framework: concepts, structure, and extensibility. Stat & Comput 2000, 10:325-337.
(link to paper)

Phung OJ, Scholle JM, Talwar M, Coleman CI. Effect of noninsulin antidiabetic drugs added to metformin therapy on glycemic control, weight gain, and hypoglycemia in type 2 diabetes. JAMA 2010, 303:1410-1418.
(link to paper)

Riley RD, Higgins JPT, Deeks JJ. Interpretation of random effects meta-analyses. BMJ 2011, 342: d549.
(link to paper)

Salanti G, Higgins JPT, Ades AE, Ioannidis JPA. Evaluation of networks of randomized trials. Stat Meth Med Res 2008, 17(3):279-301.
(link to paper)

Salanti G, Ades AE, Ioannidis JPA. Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J Clin Epidemiol 2011, 64(2):163-71.
(link to paper)

Salanti G. Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Res Synth Meth 2012, 3(2):80-97.
(link to paper)

Salanti G, Giovane CD, Chaimani A, Caldwell DM, Higgins JPT. Evaluating the quality of evidence from a network meta-analysis. Plos One 2014, 9(7):e99682.
(link to paper)

Thijs V, Lemmens R, Fieuws S. Network meta-analysis: simultaneous meta-analysis of common antiplatelet regimens after transient ischaemic attack or stroke. Eur Heart J 2008, 29:1086–1092.
(link to paper)

Veroniki AA, Mavridis D, Higgins JPT, Salanti G. Characteristics of a loop of evidence that affect detection and estimation of inconsistency: a simulation study. BMC Med Res Methodol 2014, 14(1):106.
(link to paper)

White IR. Multivariate random-effects meta-regression: Updates to mvmeta. Stata Journal (2012) 11: 255-270.
(link to paper)

White IR, Barrett JK, Jackson D, Higgins JPT. Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression. Res Synth Meth 2012, 3:111–125.
(link to paper)

White IR, 2013. A suite of Stata programs for network meta-analysis. Presented at UK Stata User's Group, London. Available from (link to presentation)

See also

metan, metareg, mvmeta, network.