netsplit | R Documentation |
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,
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 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,
subset = NULL,
nchar.trts = x$nchar.trts,
digits = gs("digits"),
digits.stat = gs("digits.stat"),
digits.pval = gs("digits.pval"),
digits.prop = max(gs("digits.pval") - 2, 2),
text.NA = ".",
backtransf = x$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 meta-analysis should be printed. |
random |
A logical indicating whether results for the random effects network meta-analysis 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 meta-analyis 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). |
subset |
An optional logical vector specifying a subset of comparisons to print (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 z-value
of test of agreement between direct and indirect evidence, see
|
digits.pval |
Minimal number of significant digits for p-value
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 p-values should be printed in scientific notation, e.g., 1.2345e-01 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 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.
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 meta-analysis (common / random effects model), i.e., data frame with columns comparison, TE, seTE, lower, upper, z, and p. |
direct.common , direct.random |
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. |
indirect.common , indirect.random |
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. |
compare.common , compare.random |
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. |
sm |
A character string indicating underlying summary measure |
level.ma |
The level used to calculate confidence intervals for pooled estimates. |
tictoc |
Computation times for node-splitting method or SIDDE (if R package tictoc is installed). |
version |
Version of R package netmeta used to create object. |
Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de, Gerta Rücker gerta.ruecker@uniklinik-freiburg.de, Orestis Efthimiou oremiou@gmail.com
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
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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