netsplit  R Documentation 
Methods to split network estimates into the contribution of direct and indirect evidence and to test for local inconsistency in network metaanalysis.
netsplit( x, method, upper = TRUE, reference.group = x$reference.group, baseline.reference = x$baseline.reference, order = NULL, sep.trts = x$sep.trts, quote.trts = "", tol.direct = 5e04, common = x$common, random = x$random, backtransf = x$backtransf, warn = FALSE, warn.deprecated = gs("warn.deprecated"), verbose = FALSE, ... ) ## S3 method for class 'netsplit' print( x, common = x$x$common, random = x$x$random, show = "all", overall = TRUE, ci = FALSE, test = show %in% c("all", "with.direct", "both"), only.reference = FALSE, sortvar = NULL, nchar.trts = x$nchar.trts, digits = gs("digits"), digits.stat = gs("digits.stat"), digits.pval = gs("digits.pval"), digits.prop = max(gs("digits.pval")  2, 2), text.NA = ".", backtransf = x$backtransf, scientific.pval = gs("scientific.pval"), big.mark = gs("big.mark"), legend = TRUE, indent = TRUE, warn.deprecated = gs("warn.deprecated"), ... )
x 
An object of class 
method 
A character string indicating which method to split
direct and indirect evidence is to be used. Either

upper 
A logical indicating whether treatment comparisons
should be selected from the lower or upper triangle of the
treatment effect matrices (see list elements 
reference.group 
Reference treatment. Ignored if argument

baseline.reference 
A logical indicating whether results
should be expressed as comparisons of other treatments versus the
reference treatment or vice versa. This argument is only
considered if 
order 
A optional character or numerical vector specifying the order of treatments in comparisons. 
sep.trts 
A character string used in comparison names as separator between treatment labels, e.g., " vs ". 
quote.trts 
A character used to print around treatment labels. 
tol.direct 
A numeric defining the maximum deviation of the direct evidence proportion from 0 or 1 to classify a comparison as providing only indirect or direct evidence, respectively. 
common 
A logical indicating whether results for the common effects network metaanalysis should be printed. 
random 
A logical indicating whether results for the random effects network metaanalysis should be printed. 
backtransf 
A logical indicating whether printed results
should be back transformed. For example, if 
warn 
A logical indicating whether warnings should be printed. 
warn.deprecated 
A logical indicating whether warnings should be printed if deprecated arguments are used. 
verbose 
A logical indicating whether progress information should be printed. 
... 
Additional arguments. 
show 
A character string indicating which comparisons should be printed (see Details). 
overall 
A logical indicating whether estimates from network metaanalyis should be printed in addition to direct and indirect estimates. 
ci 
A logical indicating whether confidence intervals should be printed in addition to treatment estimates. 
test 
A logical indicating whether results of a test comparing direct and indirect estimates should be printed. 
only.reference 
A logical indicating whether only comparisons with the reference group should be printed. 
sortvar 
An optional vector used to sort comparisons (must be of same length as the total number of comparisons). 
nchar.trts 
A numeric defining the minimum number of characters used to create unique treatment names. 
digits 
Minimal number of significant digits, see

digits.stat 
Minimal number of significant digits for zvalue
of test of agreement between direct and indirect evidence, see

digits.pval 
Minimal number of significant digits for pvalue
of test of agreement between direct and indirect evidence, see

digits.prop 
Minimal number of significant digits for direct
evidence proportions, see 
text.NA 
A character string specifying text printed for missing values. 
scientific.pval 
A logical specifying whether pvalues should be printed in scientific notation, e.g., 1.2345e01 instead of 0.12345. 
big.mark 
A character used as thousands separator. 
legend 
A logical indicating whether a legend should be printed. 
indent 
A logical indicating whether items in the legend should be indented. 
A comparison of direct and indirect treatment estimates can serve as check for consistency of network metaanalysis (Dias et al., 2010).
This function provides two methods to derive indirect estimates:
Separate Indirect from Direct Evidence (SIDE) using a backcalculation method. The direct evidence proportion as described in König et al. (2013) is used in the calculation of the indirect evidence;
Separate Indirect from Direct Design Evidence (SIDDE) as described in Efthimiou et al. (2019).
Note, for the backcalculation method, indirect treatment estimates
are already calculated in netmeta
and this function
combines and prints these estimates in a userfriendly
way. Furthermore, this method is not available for the
MantelHaenszel and noncentral hypergeometric distribution
approach implemented in netmetabin
.
For the randomeffects model, the direct treatment estimates are
based on the common betweenstudy variance τ^2 from the
network metaanalysis, i.e. the square of list element
x$tau
.
Argument show
determines which comparisons are printed:
“all”  All comparisons 
“both”  Only comparisons contributing both direct and indirect evidence 
“with.direct”  Comparisons providing direct evidence 
“direct.only”  Comparisons providing only direct evidence 
“indirect.only”  Comparisons providing only indirect evidence 
The SIDDE approach can be computeintensive in large
networks. Crude information on the computation progress is printed
for SIDDE if argument verbose
is TRUE
. In addition,
computation times are printed if R package tictoc is
installed.
An object of class netsplit
with corresponding print
and forest
functions. The object is a list containing the
following components:
common, random 
As defined above. 
comparison 
A vector with treatment comparisons. 
prop.common, prop.random 
A vector with direct evidence proportions (common / random effects model). 
common, random 
Results of network metaanalysis (common / random effects model), i.e., data frame with columns comparison, TE, seTE, lower, upper, z, and p. 
direct.common, direct.random 
Network metaanalysis results based on direct evidence (common / random effects model), i.e., data frame with columns comparison, TE, seTE, lower, upper, z, and p. 
indirect.common, indirect.random 
Network metaanalysis results based on indirect evidence (common / random effects model), i.e., data frame with columns comparison, TE, seTE, lower, upper, z, and p. 
compare.common, compare.random 
Comparison of direct and indirect evidence in network metaanalysis (common / random effects model), i.e., data frame with columns comparison, TE, seTE, lower, upper, z, and p. 
sm 
A character string indicating underlying summary measure 
level.ma 
The level used to calculate confidence intervals for pooled estimates. 
tictoc 
Computation times for SIDDE approach (if R package tictoc is installed). 
version 
Version of R package netmeta used to create object. 
Guido Schwarzer sc@imbi.unifreiburg.de, Gerta Rücker ruecker@imbi.unifreiburg.de, Orestis Efthimiou oremiou@gmail.com
Dias S, Welton NJ, Caldwell DM, Ades AE (2010): Checking consistency in mixed treatment comparison metaanalysis. Statistics in Medicine, 29, 932–44
Efthimiou O, Rücker G, Schwarzer G, Higgins J, Egger M, Salanti G (2019): A MantelHaenszel model for network metaanalysis of rare events. Statistics in Medicine, 1–21, https://doi.org/10.1002/sim.8158
König J, Krahn U, Binder H (2013): Visualizing the flow of evidence in network metaanalysis and characterizing mixed treatment comparisons. Statistics in Medicine, 32, 5414–29
Puhan MA, Schünemann HJ, Murad MH, et al. (2014): A GRADE working group approach for rating the quality of treatment effect estimates from network metaanalysis. British Medical Journal, 349, g5630
forest.netsplit
, netmeta
,
netmetabin
, netmeasures
data(Woods2010) # p1 < pairwise(treatment, event = r, n = N, studlab = author, data = Woods2010, sm = "OR") # net1 < netmeta(p1) # print(netsplit(net1), digits = 2) ## Not run: print(netsplit(net1), digits = 2, backtransf = FALSE, common = FALSE) # Sort by increasing number of studies in direct comparisons print(netsplit(net1), digits = 2, sortvar = k) # Sort by decreasing number of studies in direct comparisons print(netsplit(net1), digits = 2, sortvar = k) # Sort by increasing evidence proportion under common effects model print(netsplit(net1), digits = 2, sortvar = prop.common) # Sort by decreasing evidence proportion under common effects model print(netsplit(net1), digits = 2, sortvar = prop.common) # Sort by decreasing evidence proportion under common effects model # and number of studies print(netsplit(net1), digits = 2, sortvar = cbind(prop.common, k)) data(Senn2013) # net2 < netmeta(TE, seTE, treat1.long, treat2.long, studlab, data = Senn2013) # print(netsplit(net2), digits = 2) # Layout of Puhan et al. (2014), Table 1 print(netsplit(net2), digits = 2, ci = TRUE, test = FALSE) data(Dong2013) p3 < pairwise(treatment, death, randomized, studlab = id, data = Dong2013, sm = "OR") net3 < netmetabin(p3) netsplit(net3) ## End(Not run)
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