Methods to split network estimates into the contribution of direct and indirect evidence and to test for local inconsistency in network meta-analysis.
netsplit(
x,
method,
upper = TRUE,
reference.group = x$reference.group,
baseline.reference = x$baseline.reference,
show = gs("show"),
overall = TRUE,
direct = TRUE,
indirect = TRUE,
only.reference = FALSE,
ci = FALSE,
test = show %in% c("all", "with.direct", "both"),
order = NULL,
sep.trts = x$sep.trts,
quote.trts = "",
tol.direct = 5e-04,
common = x$common,
random = x$random,
backtransf = x$backtransf,
warn = FALSE,
warn.deprecated = gs("warn.deprecated"),
verbose = FALSE,
...
)# S3 method for netsplit
print(
x,
common = x$x$common,
random = x$x$random,
show = x$show,
overall = x$overall,
direct = x$direct,
indirect = x$indirect,
ci = x$ci,
test = x$test,
only.reference = x$only.reference,
sortvar = NULL,
subset = NULL,
nchar.trts = x$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 = gs("lab.NA"),
backtransf = x$backtransf,
scientific.pval = gs("scientific.pval"),
big.mark = gs("big.mark"),
legend = gs("legend"),
indent = TRUE,
warn.deprecated = gs("warn.deprecated"),
...
)
An object of class netsplit with corresponding print
and forest functions. The object is a list containing the
following components:
As defined above.
A vector with treatment comparisons.
A vector with direct evidence proportions (common / random effects model).
Results of network meta-analysis (common / random effects model), i.e., data frame with columns comparison, TE, seTE, lower, upper, z, and p.
Network meta-analysis results based on direct evidence (common / random effects model), i.e., data frame with columns comparison, TE, seTE, lower, upper, z, and p.
Network meta-analysis results based on indirect evidence (common / random effects model), i.e., data frame with columns comparison, TE, seTE, lower, upper, z, and p.
Comparison of direct and indirect evidence in network meta-analysis (common / random effects model), i.e., data frame with columns comparison, TE, seTE, lower, upper, z, and p.
A character string indicating underlying summary measure
The level used to calculate confidence intervals for pooled estimates.
Computation times for node-splitting method or SIDDE (if R package tictoc is installed).
Version of R package netmeta used to create object.
An object of class netmeta or netsplit.
A character string indicating which method to split
direct and indirect evidence is to be used. Either
"Back-calculation" or "SIDDE", can be abbreviated.
See Details.
A logical indicating whether treatment comparisons
should be selected from the lower or upper triangle of the
treatment effect matrices (see list elements TE.common and
TE.random in the netmeta object). Ignored if
argument order is provided.
Reference treatment. Ignored if argument
order is provided.
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 reference.group is not equal to ""
and argumentorder is not provided.
A character string indicating which comparisons should be printed (see Details).
A logical indicating whether estimates from network meta-analysis should be printed.
A logical indicating whether direct estimates should be printed.
A logical indicating whether indirect estimates should be printed.
A logical indicating whether only comparisons with the reference group should be printed.
A logical indicating whether confidence intervals should be printed in addition to treatment estimates.
A logical indicating whether results of a test comparing direct and indirect estimates should be printed.
A optional character or numerical vector specifying the order of treatments in comparisons.
A character string used in comparison names as separator between treatment labels, e.g., " vs ".
A character used to print around treatment labels.
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.
A logical indicating whether results for the common effects network meta-analysis should be printed.
A logical indicating whether results for the random effects network meta-analysis should be printed.
A logical indicating whether printed results
should be back transformed. For example, if backtransf =
TRUE, results for sm = "OR" are printed as odds ratios
rather than log odds ratios.
A logical indicating whether warnings should be printed.
A logical indicating whether warnings should be printed if deprecated arguments are used.
A logical indicating whether progress information should be printed.
Additional arguments.
An optional vector used to sort comparisons (must be of same length as the total number of comparisons).
An optional logical vector specifying a subset of comparisons to print (must be of same length as the total number of comparisons) .
A numeric defining the minimum number of characters used to create unique treatment names.
Minimal number of significant digits, see
print.default.
Minimal number of significant digits for z-value
of test of agreement between direct and indirect evidence, see
print.default.
Minimal number of significant digits for p-value
of test of agreement between direct and indirect evidence, see
print.default.
Minimal number of significant digits for direct
evidence proportions, see print.default.
A character string specifying text printed for missing values.
A logical specifying whether p-values should be printed in scientific notation, e.g., 1.2345e-01 instead of 0.12345.
A character used as thousands separator.
A logical indicating whether a legend should be printed.
A logical indicating whether items in the legend should be indented.
Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de, Gerta Rücker gerta.ruecker@uniklinik-freiburg.de, Orestis Efthimiou oremiou@gmail.com
A comparison of direct and indirect treatment estimates can serve as check for consistency of network meta-analysis (Dias et al., 2010).
This function provides two methods to derive indirect estimates:
Separate Indirect from Direct Evidence (SIDE) using a
back-calculation method (method = "Back-calculation")
based on the direct evidence proportion to calculate the
indirect evidence (König et al., 2013);
Separate Indirect from Direct Design Evidence (SIDDE) as described in Efthimiou et al. (2019).
Note, for the back-calculation method, indirect treatment estimates
are already calculated in netmeta and this function
combines and prints these estimates in a user-friendly
way. Furthermore, this method is not available for the
Mantel-Haenszel and non-central hypergeometric distribution
approach implemented in netmetabin.
For the random-effects model, the direct treatment estimates are
based on the common between-study variance \(\tau^2\) from the
network meta-analysis, 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 node-splitting method and SIDDE can be compute-intensive in
large networks. Crude information on the computation progress is
printed if argument verbose = TRUE. In addition, computation
times are printed if R package tictoc is installed.
Dias S, Welton NJ, Caldwell DM, Ades AE (2010): Checking consistency in mixed treatment comparison meta-analysis. Statistics in Medicine, 29, 932--44
Efthimiou O, Rücker G, Schwarzer G, Higgins J, Egger M, Salanti G (2019): A Mantel-Haenszel model for network meta-analysis of rare events. Statistics in Medicine, 38, 2992--3012
König J, Krahn U, Binder H (2013): Visualizing the flow of evidence in network meta-analysis 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 meta-analysis. British Medical Journal, 349, g5630
forest.netsplit, netmeta,
netmetabin, netmeasures,
dat.woods2010, Senn2013,
dat.dong2013
pw1 <- pairwise(treatment, event = r, n = N,
studlab = author, data = dat.woods2010, sm = "OR")
#
net1 <- netmeta(pw1)
#
print(netsplit(net1), digits = 2)
# \donttest{
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)
pw3 <- pairwise(treatment, death, randomized, studlab = id,
data = dat.dong2013, sm = "OR")
net3 <- netmetabin(pw3)
netsplit(net3)
# }
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