Description Usage Arguments Details Value Author(s) References See Also Examples
Network metaanalysis is a generalisation of pairwise metaanalysis that compares all pairs of treatments within a number of treatments for the same condition. The graphtheoretical approach for network metaanalysis uses methods that were originally developed in electrical network theory. It has been found to be equivalent to the frequentist approach to network metaanalysis which is based on weighted least squares regression (Rücker, 2012).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34  netmeta(
TE,
seTE,
treat1,
treat2,
studlab,
data = NULL,
subset = NULL,
sm,
level = gs("level"),
level.comb = gs("level.comb"),
comb.fixed = gs("comb.fixed"),
comb.random = gs("comb.random")  !is.null(tau.preset),
prediction = FALSE,
level.predict = gs("level.predict"),
reference.group = "",
baseline.reference = TRUE,
all.treatments = NULL,
seq = NULL,
tau.preset = NULL,
tol.multiarm = 0.001,
tol.multiarm.se = tol.multiarm,
details.chkmultiarm = FALSE,
sep.trts = ":",
nchar.trts = 666,
n1 = NULL,
n2 = NULL,
event1 = NULL,
event2 = NULL,
backtransf = gs("backtransf"),
title = "",
keepdata = gs("keepdata"),
warn = TRUE
)

TE 
Estimate of treatment effect, i.e. difference between first and second treatment (e.g. log odds ratio, mean difference, or log hazard ratio). 
seTE 
Standard error of treatment estimate. 
treat1 
Label/Number for first treatment. 
treat2 
Label/Number for second treatment. 
studlab 
An optional  but important!  vector with study labels (see Details). 
data 
An optional data frame containing the study information. 
subset 
An optional vector specifying a subset of studies to be used. 
sm 
A character string indicating underlying summary measure,
e.g., 
level 
The level used to calculate confidence intervals for individual comparisons. 
level.comb 
The level used to calculate confidence intervals for pooled estimates. 
comb.fixed 
A logical indicating whether a fixed effects (common effects) network metaanalysis should be conducted. 
comb.random 
A logical indicating whether a random effects network metaanalysis should be conducted. 
prediction 
A logical indicating whether prediction intervals should be printed. 
level.predict 
The level used to calculate prediction intervals for a new study. 
reference.group 
Reference treatment. 
baseline.reference 
A logical indicating whether results
should be expressed as comparisons of other treatments versus the
reference treatment (default) or vice versa. This argument is
only considered if 
all.treatments 
A logical or 
seq 
A character or numerical vector specifying the sequence of treatments in printouts. 
tau.preset 
An optional value for manually setting the squareroot of the betweenstudy variance τ^2. 
tol.multiarm 
A numeric for the tolerance for consistency of treatment estimates in multiarm studies which are consistent by design. 
tol.multiarm.se 
A numeric for the tolerance for consistency of standard errors in multiarm studies which are consistent by design. 
details.chkmultiarm 
A logical indicating whether treatment estimates and / or variances of multiarm studies with inconsistent results or negative multiarm variances should be printed. 
sep.trts 
A character used in comparison names as separator between treatment labels. 
nchar.trts 
A numeric defining the minimum number of characters used to create unique treatment names (see Details). 
n1 
Number of observations in first treatment group. 
n2 
Number of observations in second treatment group. 
event1 
Number of events in first treatment group. 
event2 
Number of events in second treatment group. 
backtransf 
A logical indicating whether results should be
back transformed in printouts and forest plots. If

title 
Title of metaanalysis / systematic review. 
keepdata 
A logical indicating whether original data (set) should be kept in netmeta object. 
warn 
A logical indicating whether warnings should be printed (e.g., if studies are excluded from metaanalysis due to zero standard errors). 
Network metaanalysis using R package netmeta is described in detail in Schwarzer et al. (2015), Chapter 8.
Let n be the number of different treatments (nodes, vertices) in a network and let m be the number of existing comparisons (edges) between the treatments. If there are only twoarm studies, m is the number of studies. Let TE and seTE be the vectors of observed effects and their standard errors. Let W be the mxm diagonal matrix that contains the inverse variance 1 / seTE^2.
The given comparisons define the network structure. Therefrom an mxn design matrix X (edgevertex incidence matrix) is formed; for more precise information, see Rücker (2012). Moreover, the nxn Laplacian matrix L and its MoorePenrose pseudoinverse L+ are calculated (both matrices play an important role in graph theory and electrical network theory). Using these matrices, the variances based on both direct and indirect comparisons can be estimated. Moreover, the hat matrix H can be estimated by H = XL+X^tW = X(X^t W X)^+X^tW and finally consistent treatment effects can be estimated by applying the hat matrix to the observed (potentially inconsistent) effects. H is a projection matrix which maps the observed effects onto the consistent (n1)dimensional subspace. This is the Aitken estimator (Senn et al., 2013). As in pairwise metaanalysis, the Q statistic measures the deviation from consistency. Q can be separated into parts for each pairwise metaanalysis and a part for remaining inconsistency between comparisons.
Often multiarm studies are included in a network metaanalysis. In multiarm studies, the treatment effects on different comparisons are not independent, but correlated. This is accounted for by reweighting all comparisons of each multiarm study. The method is described in Rücker (2012) and Rücker and Schwarzer (2014).
Comparisons belonging to multiarm studies are identified by
identical study labels (argument studlab
). It is therefore
important to use identical study labels for all comparisons
belonging to the same multiarm study, e.g., study label
"Willms1999" for the threearm study in the data example (Senn et
al., 2013). The function netmeta then automatically accounts for
withinstudy correlation by reweighting all comparisons of each
multiarm study.
Data entry for this function is in contrastbased format,
that is, data are given as contrasts (differences) between two
treatments (argument TE
) with standard error (argument
seTE
). In principle, metaanalysis functions from R package
meta, e.g. metabin
for binary outcomes or
metacont
for continuous outcomes, can be used to
calculate treatment effects separately for each treatment
comparison which is a rather tedious enterprise. If data are
provided in armbased format, that is, data are given for
each treatment arm separately (e.g. number of events and
participants for binary outcomes), a much more convenient way to
transform data into contrastbased form is available. Function
pairwise
can automatically transform data with binary
outcomes (using the metabin
function from R package
meta), continuous outcomes (metacont
function), incidence rates (metainc
function), and
generic outcomes (metagen
function). Additional
arguments of these functions can be provided, e.g., to calculate
Hedges' g or Cohen's d for continuous outcomes (see
help page of function pairwise
).
Note, all pairwise comparisons must be provided for a multiarm
study. Consider a multiarm study of p treatments with known
variances. For this study, treatment effects and standard errors
must be provided for each of p(p  1) / 2 possible
comparisons. For instance, a threearm study contributes three
pairwise comparisons, a fourarm study even six pairwise
comparisons. Function pairwise
automatically
calculates all pairwise comparisons for multiarm studies.
A simple random effects model assuming that a constant
heterogeneity variance is added to each comparison of the network
can be defined via a generalised methods of moments estimate of the
betweenstudies variance τ^2 (Jackson et al., 2012). This
is added to the observed sampling variance seTE^2
of each
comparison in the network (before appropriate adjustment for
multiarm studies). Then, as in standard pairwise metaanalysis,
the procedure is repeated with the resulting enlarged standard
errors.
For the randomeffects model, the direct treatment estimates are based on the common betweenstudy variance τ^2 from the network metaanalysis.
Internally, both fixed effects and random effects models are
calculated regardless of values choosen for arguments
comb.fixed
and comb.random
. Accordingly, the network
estimates for the random effects model can be extracted from
component TE.random
of an object of class "netmeta"
even if argument comb.random = FALSE
. However, all functions
in R package netmeta will adequately consider the values for
comb.fixed
and comb.random
. E.g. function
print.summary.netmeta
will not print results for the
random effects model if comb.random = FALSE
.
By default, treatment names are not abbreviated in
printouts. However, in order to get more concise printouts,
argument nchar.trts
can be used to define the minimum number
of characters for abbreviated treatment names (see
abbreviate
, argument minlength
). R function
treats
is utilised internally to create abbreviated
treatment names.
Names of treatment comparisons are created by concatenating
treatment labels of pairwise comparisons using sep.trts
as
separator (see paste
). These comparison names are
used in the covariance matrices Cov.fixed
and
Cov.random
and in some R functions, e.g,
decomp.design
. By default, a colon is used as the
separator. If any treatment label contains a colon the following
characters are used as separator (in consecutive order):
""
, "_"
, "/"
, "+"
, "."
,
""
, and "*"
. If all of these characters are used in
treatment labels, a corresponding error message is printed asking
the user to specify a different separator.
An object of class netmeta
with corresponding print
,
summary
, forest
, and netrank
functions. The
object is a list containing the following components:
studlab, treat1, treat2, TE, seTE 
As defined above. 
seTE.adj 
Standard error of treatment estimate, adjusted for multiarm studies. 
n1, n2, event1, event2 
As defined above. 
k 
Total number of studies. 
m 
Total number of pairwise comparisons. 
n 
Total number of treatments. 
d 
Total number of designs (corresponding to the unique set of treatments compared within studies). 
trts 
Treatments included in network metaanalysis. 
k.trts 
Number of studies evaluating a treatment. 
n.trts 
Number of observations receiving a treatment (if
arguments 
events.trts 
Number of events observed for a treatment (if
arguments 
multiarm 
Logical vector to identify pairwise comparisons from multiarm studies. 
n.arms 
Number of treatment arms in study providing pairwise comparison. 
studies 
Vector with unique study labels. 
narms 
Number of arms for each study. 
designs 
Vector with unique designs present in the network. A design corresponds to the set of treatments compared within a study. 
TE.nma.fixed, TE.nma.random 
A vector of length m of consistent treatment effects estimated by network metaanalysis (nma) (fixed effects / random effects model). 
seTE.nma.fixed, seTE.nma.random 
A vector of length m of effective standard errors estimated by network metaanalysis (fixed effects / random effects model). 
lower.nma.fixed, lower.nma.random 
A vector of length m of lower confidence interval limits for consistent treatment effects estimated by network metaanalysis (fixed effects / random effects model). 
upper.nma.fixed, upper.nma.random 
A vector of length m of upper confidence interval limits for the consistent treatment effects estimated by network metaanalysis (fixed effects / random effects model). 
zval.nma.fixed, zval.nma.random 
A vector of length m of zvalues for test of treatment effect for individual comparisons (fixed effects / random effects model). 
pval.nma.fixed, pval.nma.random 
A vector of length m of pvalues for test of treatment effect for individual comparisons (fixed effects / random effects model). 
leverage.fixed 
A vector of length m of leverages, interpretable as factors by which variances are reduced using information from the whole network. 
w.fixed, w.random 
A vector of length m of weights of individual studies (fixed effects / random effects model). 
Q.fixed 
A vector of length m of contributions to total heterogeneity / inconsistency statistic. 
TE.fixed, TE.random 
nxn matrix with estimated overall treatment effects (fixed effects / random effects model). 
seTE.fixed, seTE.random 
nxn matrix with standard errors (fixed effects / random effects model). 
lower.fixed, upper.fixed, lower.random,
upper.random 
nxn matrices with lower and upper confidence interval limits (fixed effects / random effects model). 
zval.fixed, pval.fixed, zval.random,
pval.random 
nxn matrices with zvalue and pvalue for test of overall treatment effect (fixed effects / random effects model). 
seTE.predict 
nxn matrix with standard errors for prediction intervals. 
lower.predict, upper.predict 
nxn matrices with lower and upper prediction interval limits. 
prop.direct.fixed, prop.direct.random 
A named vector of the direct evidence proportion of each network estimate. (fixed effects / random effects model). 
TE.direct.fixed, TE.direct.random 
nxn matrix with estimated treatment effects from direct evidence (fixed effects / random effects model). 
seTE.direct.fixed, seTE.direct.random 
nxn matrix with estimated standard errors from direct evidence (fixed effects / random effects model). 
lower.direct.fixed, upper.direct.fixed, lower.direct.random,

nxn matrices with lower and upper confidence interval limits from direct evidence (fixed effects / random effects model). 
upper.direct.random 
nxn matrices with lower and upper confidence interval limits from direct evidence (fixed effects / random effects model). 
zval.direct.fixed, pval.direct.fixed, zval.direct.random,

nxn matrices with zvalue and pvalue for test of overall treatment effect from direct evidence (fixed effects / random effects model). 
pval.direct.random 
nxn matrices with zvalue and pvalue for test of overall treatment effect from direct evidence (fixed effects / random effects model). 
TE.indirect.fixed, TE.indirect.random 
nxn matrix with estimated treatment effects from indirect evidence (fixed effects / random effects model). 
seTE.indirect.fixed, seTE.indirect.random 
nxn matrix with estimated standard errors from indirect evidence (fixed effects / random effects model). 
lower.indirect.fixed, upper.indirect.fixed,
lower.indirect.random, 
nxn matrices with lower and upper confidence interval limits from indirect evidence (fixed effects / random effects model). 
upper.indirect.random 
nxn matrices with lower and upper confidence interval limits from indirect evidence (fixed effects / random effects model). 
zval.indirect.fixed, pval.indirect.fixed,
zval.indirect.random, 
nxn matrices with zvalue and pvalue for test of overall treatment effect from indirect evidence (fixed effects / random effects model). 
pval.indirect.random 
nxn matrices with zvalue and pvalue for test of overall treatment effect from indirect evidence (fixed effects / random effects model). 
Q 
Overall heterogeneity / inconsistency statistic. 
df.Q 
Degrees of freedom for test of heterogeneity / inconsistency. 
pval.Q 
Pvalue for test of heterogeneity / inconsistency. 
I2, lower.I2, upper.I2 
Isquared, lower and upper confidence limits. 
tau 
Squareroot of betweenstudy variance. 
Q.heterogeneity 
Overall heterogeneity statistic. 
df.Q.heterogeneity 
Degrees of freedom for test of overall heterogeneity. 
pval.Q.heterogeneity 
Pvalue for test of overall heterogeneity. 
Q.inconsistency 
Overall inconsistency statistic. 
df.Q.inconsistency 
Degrees of freedom for test of overall inconsistency. 
pval.Q.inconsistency 
Pvalue for test of overall inconsistency. 
Q.decomp 
Data frame with columns 'treat1', 'treat2', 'Q', 'df' and 'pval.Q', providing heterogeneity statistics for each pairwise metaanalysis of direct comparisons. 
A.matrix 
Adjacency matrix (nxn). 
X.matrix 
Design matrix (mxn). 
B.matrix 
Edgevertex incidence matrix (mxn). 
L.matrix 
Laplacian matrix (nxn). 
Lplus.matrix 
MoorePenrose pseudoinverse of the Laplacian matrix (nxn). 
Q.matrix 
Matrix of heterogeneity statistics for pairwise metaanalyses, where direct comparisons exist (nxn). 
G.matrix 
Matrix with variances and covariances of comparisons (mxm). G is defined as BL+B^t. 
H.matrix 
Hat matrix (mxm), defined as H = GW = BL+B^tW. 
n.matrix 
nxn matrix with number of
observations in direct comparisons (if arguments 
events.matrix 
nxn matrix with number of events
in direct comparisons (if arguments 
P.fixed, P.random 
nxn matrix with direct evidence proportions (fixed effects / random effects model). 
Cov.fixed 
Variancecovariance matrix (fixed effects model) 
Cov.random 
Variancecovariance matrix (random effects model) 
sm, level, level.comb 
As defined above. 
comb.fixed, comb.random 
As defined above. 
prediction, level.predict 
As defined above. 
reference.group, baseline.reference, all.treatments 
As defined above. 
seq, tau.preset, tol.multiarm, tol.multiarm.se 
As defined above. 
details.chkmultiarm, sep.trts, nchar.trts 
As defined above. 
backtransf, title, warn 
As defined above. 
call 
Function call. 
version 
Version of R package netmeta used to create object. 
Gerta Rücker ruecker@imbi.unifreiburg.de, Guido Schwarzer sc@imbi.unifreiburg.de
Jackson D, White IR, Riley RD (2012): Quantifying the impact of betweenstudy heterogeneity in multivariate metaanalyses. Statistics in Medicine, 31, 3805–20
Rücker G (2012): Network metaanalysis, electrical networks and graph theory. Research Synthesis Methods, 3, 312–24
Rücker G, Schwarzer G (2014): Reduce dimension or reduce weights? Comparing two approaches to multiarm studies in network metaanalysis. Statistics in Medicine, 33, 4353–69
Schwarzer G, Carpenter JR, Rücker G (2015): MetaAnalysis with R (UseR!). Springer International Publishing, Switzerland
Senn S, Gavini F, Magrez D, Scheen A (2013): Issues in performing a network metaanalysis. Statistical Methods in Medical Research, 22, 169–89
pairwise
, forest.netmeta
,
netrank
, metagen
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33  data(Senn2013)
# Conduct fixed effects network metaanalysis
#
net1 < netmeta(TE, seTE, treat1, treat2, studlab,
data = Senn2013, sm = "MD",
comb.random = FALSE)
net1
net1$Q.decomp
# Comparison with reference group
#
print(net1, reference = "plac")
## Not run:
# Conduct random effects network metaanalysis
#
net2 < netmeta(TE, seTE, treat1, treat2, studlab,
data = Senn2013, sm = "MD",
comb.fixed = FALSE)
net2
# Change printing order of treatments with placebo last and use
# long treatment names
#
trts < c("acar", "benf", "metf", "migl", "piog",
"rosi", "sita", "sulf", "vild", "plac")
net3 < netmeta(TE, seTE, treat1.long, treat2.long, studlab,
data = Senn2013, sm = "MD", comb.fixed = FALSE,
seq = trts, reference = "Placebo")
print(summary(net3), digits = 2)
## End(Not run)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.