*! version 1.1 27may2015

Title

network meta -- Perform network meta-analysis under consistency or inconsistency model

Syntax

network meta [consistency|inconsistency] [if] [in]
[,
mvmeta_options]

regress(varlist) Specify covariates for network meta-regression.
Every treatment contrast is allowed to depend on the covariate(s) listed.
This option is currently only allowed in augmented format.

equations(suboptions) Equations option for mvmeta:
can be used to perform network meta-regression.
If variables _y_* hold the treatment effects then eq(_y_*:x) is equivalent to regress(x).
This option is only sensible in augmented format.

luades[(treatment_list)] For the inconsistency model,
this option gives the model of Lu and Ades (2006)
as formalised by Higgins et al (2012).
With only two-arm studies, this is the same as the design-by-treatment interaction model
of Higgins et al (2012).
With multi-arm studies, the Lu-Ades model is smaller than the design-by-treatment interaction model
and depends on the treatment ordering.
The optional argument specifies an ordering of the treatments;
the reference treatment should not be included in this argument as it is always placed first.
(The reference treatment can be changed by network convert).
This option is only available in augmented format.

force Force model fitting when network meta detects one of the difficulties described below.

nowarnings Suppress warning messages (not recommended).

mvmeta_options Other options for mvmeta:
for example, bscov(uns) could be used to over-ride the standard "common heterogeneity" model.

Description

network meta defines a model to be fitted:
either the consistency model or the
design-by-treatment interaction inconsistency model.
It can handle data in any of the three network formats:
if data are in the augmented or standard formats
then the models are fitted using mvmeta;
if data are in the pairs formats
then the models are fitted using metareg.
mvmeta or metareg must have been installed.

network meta fits the chosen model. It then stores the fitted values in a matrix
(by default called _network_consistency or _network_inconsistency)
for use in network forest.
If the inconsistency model is fitted, then a Wald test for inconsistency is also defined and performed.
The mvmeta or metareg command used
can be recalled by pressing F9.
The command to test for inconsistency, if used, can be recalled by pressing F8.

By default, network meta uses a structured between-studies covariance matrix which
assumes that all treatment contrasts have the same heterogeneity variance.

If neither consistency nor inconsistency is specified then the previous mvmeta model is rerun.

Which network format is best?

You should be able to get identical results using the standard and augmented formats.
You should be able to match these results using the pairs format
provided you have only two-arm trials,
but if you have multi-arm trials then the results using the pairs format will be wrong.
If you want to estimate the best treatment (see network rank),
then you will need to fit the models using the augmented format.

Difficulties

network meta attempts to spot various difficulties that may make particular
network meta-analysis models unsuitable for your data.
The diagnostics are computed by network setup.
If instead you used network import you will get a warning that the diagnostics cannot be done.

1. A disconnected network arises when the treatments can be divided into
two or more groups in such a way that no studies make comparisons between groups.
In this case, network meta is unable to fit models and you should fit models separately by component - e.g. network meta c if _component==1.

2. A network has no degrees of freedom for inconsistency when it is tree-shaped
(that is, when there are no loops in the network).
In this case, network meta will be able to fit consistency models but will refuse to fit inconsistency models.

3. A network has no degrees of freedom for heterogeneity when no design is represented by multiple studies.
In this case, network meta will refuse to fit random-effects models.
If there are degrees of freedom for inconsistency then you will be able to fit random-effects consistency models, since these treat inconsistency as heterogeneity:
you will need to override network meta's default with the force option.

Examples

Fit the consistency model:

. network meta consistency

Fit the consistency model with a general between-studies covariance structure:

. network meta consistency, bscov(unstructured)

Fit the consistency model with all treatment effects depending on covariate x:

. network meta consistency, eq(_y*:x)

Short way to do the same:

. network meta consistency, regress(x)

Fit the inconsistency model:

. network meta inconsistency

Return to main help page for network