*! version 1.3.2 30aug2017

*! version 1.1 27may2015

Title

network -- Suite of commands for network meta-analysis

Syntax

Get started

network setup set up data from arm-specific counts

network import import data set of pairwise comparisons

Descriptive tables and figures

network table tabulate data

network pattern pattern of network

network map map of network

Utilities

network convert convert data between formats

network query display network settings

network unset delete network settings (rarely needed)

Analyses

network meta perform network meta-analysis

network rank rank treatments after network meta-analysis

network sidesplit fit side-splitting (node-splitting) model(s)

network compare tabulate all comparisons estimated from the network

Graphs of results

network forest forest plot

Description

network is a suite of programs for
importing data for network meta-analysis,
running a contrast-based network meta-analysis using mvmeta or metareg,
assessing inconsistency,
and graphing the data and results.
The data in each arm of each study are assumed to be available
either as binomial counts (successes/total)
or as mean, standard deviation and number of individuals for a quantitative variable.

network is primarily aimed to simplify the task of data analysis.
However, it also has several features of interest to methodologists:
see Features of interest to methodologists.

Data formats

Three data formats are available.
They are most easily illustrated in the context of a network meta-analysis involving treatments A, B and C,
with A as reference and using default variable naming.

In format augmented, the data are the contrasts of each treatment with the reference treatment.
Thus _y_B compares B with A, while _y_C compares C with A.
In any trial which does not include a reference treatment arm,
such an arm is created ("augmented") to contain a very small amount of data.

In format standard, the data are contrasts with each trial's baseline treatment.
Thus _y_1 compares each trial's 2nd treatment with its first: _y_1 compares B with A in an A-B trial and C with B in a B-C trial.
An A-B-C trial would be represented by _y_1 (B vs. A) and _y_2 (C vs. A).

In format pairs, the data are all possible contrasts between the trial's treatments.
Thus a two-arm B-C trial is represented by a single record _y (C vs. B),
while a three-arm, A-B-C trial is represented by three records comparing B vs. A, C vs. A and C vs. B.

Features of interest to methodologists

network makes it easy to show that results using the "augmented" data format are
numerically almost identical to results using the "standard" data format
(and also to results using the "pairs" data format, in the absence of multi-arm trials).
It also makes it easy to show that results are not affected by choice of reference category.

network sidesplit introduces a small modification to the procedure of Dias et al.
This makes side-splitting symmetrical even in the presence of multi-arm trials
(that is, splitting A vs. B gives the same model as splitting B vs. A).

network forest offers a new data display which generalises the forest plot for pairwise meta-analysis.

Troubleshooting and limitations

network can handle only up to about 90 treatments.
This is because of the large number of variables required in augmented format.
network setup is not currently able to set the data up directly in other formats,
but goes via augmented format.

I-squared is not usually available after network meta.
Although mvmeta's i2 option can be called, it usually fails because of network sparsity.
We are working on a suitable I-squared measure for network meta-analysis.

If a full forest plot is drawn,
then the inconsistency model must be the last mvmeta model fitted:
that is, neither network sidesplit nor network meta, c
must have been fitted since network meta, i.

network map gives poor results if a monochrome color scheme is used.
Try set scheme s2color.

Unlike other commands, network table orders the treatments in alphabetical order of their names,
not of their codes.

network rank and network sidesplit only work when the data are in augmented format.

A page of frequently asked questions is available at http://www.homepages.ucl.ac.uk/~rmjwiww/stata/meta/networkFAQs.htm.

Examples

Load the smoking data

. use http://www.homepages.ucl.ac.uk/~rmjwiww/stata/meta/smoking, clear

. network setup d n, studyvar(stud) trtvar(trt)

Draw a network graph using networkplot if installed

. network map

Fit consistency model

. network meta c

Rank treatments, noting that larger treatment effects indicate better treatments

. network rank max

Fit inconsistency model

. network meta i

Forest plot of results, adding a title and reducing the square size from its default of *0.2

. network forest, title(Smoking network) msize(*0.15)

Explore inconsistency by side-splitting

. network sidesplit all

Load the thrombolytics data

. use http://www.homepages.ucl.ac.uk/~rmjwiww/stata/meta/thromb.dta, clear

. network setup r n, studyvar(study) trtvar(treat)

Draw a network map using networkplot if installed

. network map

Improve this map

. network map, improve

. network map, triangular(5) improve

Fit consistency model

. network meta c

Rank treatments using a rankogram,
noting that smaller treatment effects indicate better treatments

. network rank min, line cumul

Fit inconsistency model

. network meta i

Forest plot of results, adding titles, using a hollow plotting symbol,
and reducing the size of the text labelling the contrasts

. network forest, xtitle(Log odds ratio and 95% CI) title(Thrombolytics network) msym(Sh) contrastopt(mlabsize(small))

Explore inconsistency by side-splitting

. network sidesplit all

Details

Various parts of the network suite require the additional program mvmeta.

In pairs format, network meta requires the additional program metareg.

network map requires the additional program networkplot.

Known problems

With large numbers of treatments, you may run into memory problems.
Please try increasing maxvar and matsize if your version of Stata allows you to do so.
The problem arises with augmented format and I hope to find a workaround in future.

Please report any other problems to ian.white@ucl.ac.uk.

Changes from version 1.2.1 to version 1.5

I have made some bug fixes and minor improvements to network setup, network meta and network forest.

network compare may be run after network meta consistency.
It outputs a table of all comparisons estimated from the network.

network setup assesses the network to see whether it is disconnected.

Future developments

network bayes (currently under test) writes WinBUGS models,
runs WinBUGS and reads the MCMC samples back into Stata.

network import from standard format.

References

Dias S, Welton NJ, Caldwell DM, Ades AE.
Checking consistency in mixed treatment comparison meta-analysis.
Statistics in Medicine 2010;29:932-944.
http://onlinelibrary.wiley.com/doi/10.1002/sim.3767/abstract

Higgins JPT, Jackson D, Barrett JL, Lu G, Ades AE, White IR.
Consistency and inconsistency in network meta-analysis:
concepts and models for multi-arm studies.
Research Synthesis Methods 2012; 3: 98-110.
http://onlinelibrary.wiley.com/doi/10.1002/jrsm.1044/abstract

Lu G, Ades AE.
Assessing evidence inconsistency in mixed treatment comparisons.
Journal of the American Statistical Association 2006; 101: 447-459.
http://amstat.tandfonline.com/doi/abs/10.1198/016214505000001302

Salanti G, Ades A, Ioannidis J.
Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis:
an overview and tutorial. Journal of Clinical Epidemiology 2011; 64: 163-171.
http://www.ncbi.nlm.nih.gov/pubmed/20688472

White IR. Multivariate random-effects meta-analysis.
Stata Journal 2009; 9: 40-56.
http://www.stata-journal.com/article.html?article=st0156

White IR. Multivariate random-effects meta-regression: Updates to mvmeta.
Stata Journal 2011; 11: 255-270.
http://www.stata-journal.com/article.html?article=st0156_1

White IR, Barrett JK, Jackson D, Higgins JPT.
Consistency and inconsistency in network meta-analysis:
model estimation using multivariate meta-regression.
Research Synthesis Methods 2012; 3: 111-125.
http://onlinelibrary.wiley.com/doi/10.1002/jrsm.1045/abstract

White IR, Thomas J.
Standardised mean differences in individually-randomised and cluster-randomised trials,
with applications to meta-analysis.
Clinical Trials 2005; 2: 141-151.
http://ctj.sagepub.com/content/2/2/141.short

Author and updates

Ian White, MRC Clinical Trials Unit at UCL, London, UK.
Email ian.white@ucl.ac.uk.

You can get the latest version of this and my other Stata software using
net from http://www.homepages.ucl.ac.uk/~rmjwiww/stata/.

See Also

mvmeta (if installed)

metareg (if installed)

Programs by Anna Chaimani: net from http://www.mtm.uoi.gr